an introduction to weather normalization of utility bills for alternative energy contractors
Utility bill tracking: there are more and more transcripts from alternative energy contractors who want to prove to their customers the savings they expect.
Customers often want to know that they have saved energy and costs as promised.
From the customer\'s point of view, the simplest and easiest to understand proof of energy conservation comes from a simple comparison of electricity charges.
Does the system save electricity? (1)
In theory, a simple comparison of pre-
Installation bill mailed
Install the bill and you will see if you have saved it.
But if it\'s so easy, why write a paper on it?
It\'s not easy.
Let\'s find out why. Figure 1.
1: expected before and after
Transformation and use of chilled water system. (
Http: suppose a solar contractor installed a new solar system for a building.
One may expect to save energy and costs from this transformation. Figure 1.
1 present the results that our alternative energy contractors may expect.
But what if the bill shows the disaster shown in Figure 1. 2? Figure 1.
2. Disaster of a project?
Comparison of Pre-
Transformation and postTransformation data (
Http: Imagine showing customers these results after they have invested hundreds of billions of dollars in your system.
Results like this can hardly inspire confidence in your abilities.
How should solar contractors present this data to their customers?
Do you think the contractors are confident about the work and the recommendation for future solar projects? Probably not.
Customers may simply look at the numbers because they don\'t lie, so they come to the conclusion that they hired the wrong contractor and the solar system didn\'t work very well!
There are many reasons why the system may not achieve the expected savings.
One possibility is that the project is saving money, but the summer after installation is much hotter than the summer before installation.
Hot summer can lead to higher air conditioning loads, which can lead to higher utility charges.
In our case, the hot summer> higher air conditioning load> higher summer utility costs, which we claim is because
It\'s hotter to install, and although the solar power project does, it doesn\'t look like it saves any energy.
Imagine explaining this to the customer!
If the weather is really the reason for the increase in usage, then how do you use utility bills to measure savings on solar projects?
The number of your savings will depend on the weather.
There is no value in savings numbers (
Unless the weather is the same year after year).
Our example may seem a bit exaggerated, but it raises the question: will weather really have such a big impact on savings numbers?
It\'s OK, but it\'s not that extreme usually.
The summer of 2005 is the hottest summer of the century.
Stay in Detroit, Michigan
Compared to the usual 12 days, there are 18 days of 90 degrees Fahrenheit or above.
In addition, the average temperature in Detroit is 74.
8 °F compared to normal 71. 4 °F.
At first glance, 3 degrees doesn\'t seem to matter, however, if you convert the temperature to the number of days of cooling (2)
As shown in Figure 1.
The result looks dramatic.
Compared with June to August, there were 909 cooling days in 2005, compared with 442 cooling days in 2004.
This is more than double!
The number of cooling days is roughly proportional to the relative building cooling requirements.
So, for Detroit, one can infer the average building needed (
And may be consumed)
The cooling energy in the summer of 2005 is more than twice that in the summer of 2004.
It is likely that there are several solar contractors in the Midwest facing this problem! Figure 1.
3: cooling days of 2004 and 2005 in Detroit, Michigan (
Http: In this case, how does the solar contractor demonstrate the savings of the solar power system?
Simply comparing utility bills won\'t work because the expected savings will be covered up by the increased cold load.
The solution is to apply the same weather data in some wayand post-
Install the bill.
Then extreme weather will not be punished.
This is the role of weather normalization.
Display savings after renovation (
Or good practices for alternative energy)
To avoid our catastrophic example, alternative energy contractors should normalize the cost of weather utilities so that changes in weather conditions do not compromise savings figures.
With more and more energy managers, energy engineers, the practice of normalizing energy bills with energy software is becoming popular, and contractors correct bills for weather reasons, because they want to prove that they are actually trying to save energy.
There are many names for this process: weather correction, normalization of weather, adjustment of weather or return of weather.
When we use weather normalization, how does weather normalization work, instead of comparing last year\'s usage with this year\'s usage, let\'s compare how much energy we will use this year, how much energy we use this year.
Many people in our industry do not think that the result of this comparison is \"saving\", but \"Avoiding use\" or \"avoiding costs \"(
If compare cost).
But since we are trying to keep this chapter at the introductory level, we will simply use the word \"save.
When we tried to compare last year\'s usage to this year\'s usage, we saw Figure 1.
A disastrous project.
We used the equation: Savings = usage for last year-when we normalized the weather, usage for this year was the same as the data in figure 1.
4, and use the equation: Save = how much energy we will use this year-use Figure 1 for this year.
4. Comparison between baseline and actual (Post-Retrofit)
Data on weather correction (
Http: The next question is, how do we calculate how much energy we will use this year?
This is why the weather is normalized.
First of all, we choose the utility bill for one year (3)
We want to compare future usage.
This is usually the year before you start an alternative energy project, the year before the installation and renovation, or the year before you, the new energy contractor, were hired, or just the past year when you want to compare current usage.
In this example, we will select the year of utility data before installing the solar power system.
We will call this year the foundation year (4). Figure 1.
5: Days of cooling (
Http: then we calculate the number of degree days for the base year billing period.
Since this example only involves cooling, we only need to collect the number of days of cooling (
Days without heating).
There is a section later in this chapter about the calculation of the number of degree days.
At present, it is only necessary to recognize that the number of cooling days need to be collected at this step. (5)Figure 1.
Cooling days for 5 years. Figure 1.
6. Find the relationship between usage and weather data.
The Blue Point represents utilities.
The red line is the most suitable line. (
Http: to establish the relationship between usage and weather, we found the line closest to all the bills.
This line is the most suitable one, found using statistical regression techniques in canned utility bill tracking software and spreadsheets.
The next step is to ensure that the best fit line is good enough to use.
The quality of the best fitting line is represented by statistical indicators, the most common of which is the R2 value.
The R2 value represents the goodness of fit, R2> 0 in the field of energy engineering.
75 is considered acceptable.
Some meters have little or no sensitivity to the weather, or may have other unknown variables that have a greater impact on use than the weather.
The R2 value of these meters may be very low.
You can generate R2 values for fit line in Excel or other canned utility bill tracking software. (6)
There is an equation for this optimal fitting line, which we call a fitting line equation, or in this case, a baseline equation. (7)
Fitting line equation in figure 1.
6 may be: baseline kWh = (
5 KW hours/day * days)+ (
417 kw h/CDD * CDD)
Once we have this equation, we complete the regression process.
Let\'s review what we did: We standardized weather data for basic year utility bills and days in our bills.
We drew standardized benchmark annual utility data and standardized weather data.
We found the most suitable line through the data.
The most appropriate line then represents the utility bill for the benchmark year.
The best fit line equation represents the best fit line, while the best fit line represents the base year of utility data.
Benchmark year Bill = best fit line = fitting line equation represents how the customer uses energy in the benchmark year and will continue to use it in the future (
Respond to changing weather conditions)
It is assumed that there is no major change in the consumption pattern of buildings.
Once you have the baseline equation, you can determine if you save any energy. How?
You collect the bill from a certain billing period after the base year. You (
Or your software)
Insert the days of the bill and the cooling days of the billing period into the baseline equation.
Assuming that there are 30 days of billing for the current month, the CDD associated with the billing period is 100. Benchmark = (
5 KW hours/day * days)+ (
417 kw h/CDD * CDD)Benchmark = (5 kWh/Day * 30)+ (
417 KW/CDD * 100)
Baseline kWh = 41,850 kWhRemember, the baseline equation indicates how the customer uses energy during the base year.
Therefore, with the new input of days and degree days, the baseline equation will tell you how much energy the building will use this year based on the base year usage pattern and this year\'s situation (
Weather and days).
We call this usage determined by the baseline equation, the baseline use.
Now, to get a fair estimate of energy savings, we compare: saving = how much energy we will use this year-how much energy we do use this year, or if we change the terms a little: savings = baseline energy use-actual energy use, where baseline energy use is calculated by the baseline equation, using the weather and days of the month, and the actual energy use is the bill for the month.
The first two equations are the same, with baseline = \"how much energy we will use this year\", which actually represents \"how much energy we do use this year \".
So, in our case, let\'s say the bill for this month is 30,000 KW: savings = baseline energy use-actual energy use = 41,850 KW hours-30,000 KW hours savings = 11,850 KW hours calculate the degree day and find the balance point cool degree day (CDD)
Roughly proportional to the energy used to cool the building, and the number of days of heating ,(HDD)
Roughly proportional to the energy used to heat a building.
Although the number of degree days is simple to calculate, it is very useful in the energy meter calculation.
They are calculated for each day and then summed for a period of time (
A month, a year, etc. ). (8)Figure 1.
7: determine the balance point using kWh/day
Outdoor temperature chart (
Http: In general, the number of degree days per day is the difference between the building balance point and the average outdoor temperature.
So, to understand living, we need to understand the concept of balance point first.
Buildings have their own set of equilibrium points for heating and cooling-they may not be the same.
The heating equilibrium point can be defined as the outdoor temperature at which the building starts heating.
In other words, when the outdoor temperature drops below the heating balance point, the heating system of the building will start.
Instead, the building starts to cool when the outdoor temperature is higher than the cooling balance point. (9)
The balance point of the building is determined by almost everything associated with it, because almost every component associated with the building has a certain impact on the heating of the building: building envelope (
Insulation values, shadows, windows, etc. )
, Temperature setting point, thermostat setting schedule (if any), the number of devices that generate heat (and people)
Lighting intensity, ventilation, HVAC system type, HVAC system Schedule, lighting and miscellaneous equipment schedule in the building, among other factors.
In the past, before energy professionals used computer and utility manager software in their daily tasks, degree day analysis was simplified by assuming that the balance point of heating and cooling was 65 degrees Fahrenheit.
Therefore, it is easy to publish and distribute degree days as everyone calculates degree days using the same criteria (
That is, the balance point is 65 °F).
However, it is more accurate to recognize that each building has its own balance point and calculate the number of degree days accordingly.
Therefore, it is unlikely that you will see the number of degree days available, as more complex analysis requires you to calculate your degree days based on the balance point of your own building. (10)Figure 1.
8: average temperature between kWh/day and outdoor (
Http: to find the equilibrium temperature of the building, please compare the usage/day to the average outdoor temperature (
Billing cycle)
As shown in Figure 1. 7.
Please note that figure 1.
There are two trends.
One trend is flat and the other is tilted up and right.
We draw a red line that represents two trends in figure 1. 8. (
Ignore the vertical red line for the time being. )
Flat trend represents Non
Temperature-sensitive consumption is an electrical consumption that is not related to the weather. In Figure 1. 7, Non-
The temperature consumption is roughly the same every month, about 2450 degrees per day.
Example of non
Temperature-sensitive consumption includes lighting, computers, various plug loads, industrial equipment and well pumps.
Any use above the horizontal red line is called temperature sensitive consumption, representing electrical use related to the building cooling system.
Note that in figure 1.
8, temperature sensitive consumption occurs only at temperatures greater than 61 °F.
The intersection of these two trends is called the equilibrium point, that is, the equilibrium point temperature, 61 °F in this case.
Note that in figure 1.
8. Consumption increases with the increase of outdoor temperature.
The building uses more energy as the outdoor temperature rises, so the meter is used for cooling instead of heating.
The equilibrium temperature we find is the cooling equilibrium temperature (
Not the temperature of the heating equilibrium point). Figure 1.
9: average temperature of kWh/day and outdoor (
Http: for heating use in figure 1, we can view the same type of chart. 9.
Note that the main difference between the two charts is that the temperature sensitive trend is tilted up and left (
Not up and right).
As the outdoor temperature drops, the building uses more power to heat the building.
Now that we have determined the temperature of the equilibrium point, we have all the information we need to calculate the number of days of the degree.
If your chart is similar to Figure 1
9, you will use the number of days of heating.
If your chart is similar to Figure 1.
8. You will use the cooling days. Figure 1.
10: normal daily use of production and weather.
The baseline equation is shown at the bottom of the graph (
Http: normal other VARIABLESMore and energy experts start to understand the value specification practical data production (or instead of)weather.
This works if you have a simple variable to quantify your production.
For example, a computer assembly plant can track the number of computers produced.
If a factory produces several different products, such as disk drives, desktop computers and printers, it may be difficult to come up with a single variable to represent the production of the entire factory (i. e.
Tons of products).
However, from the analysis is done on a level of 1 m, not on a level, if you have meters (or submeters)
This only serves a production line, then you can standardize the use of the products produced with that line from 1 m. Figure 1.
10 shows the standardized daily use and production of the widget factory.
The normalized baseline equation is shown at the bottom of the graph.
Notify the production unit (UPr)
And cooling days (CDD)
This means that this normalization includes weather data and production data.
School districts, colleges and universities usually normalize their school calendars.
Property issues, normal stay at Hotel and prison.
Basically, any variable can be used for normalization as long as it is an accurate, consistent predictor of the energy usage pattern.
Again, these normalization can be performed through dedicated utility bill tracking software or using spreadsheets.
The weather is different every year.
So it\'s hard to know if the change in your utility bill is due to weather fluctuations, whether it\'s due to your alternative energy system, or both.
If you want to use utility bills to determine how much energy-efficient an alternative energy system is, it is critical to remove weather variability from your energy-saving equation.
This is done using the weather normalization technique described in this article.
You can also adjust the use of other variables, such as occupancy or production. --1)
What is the alternative?
The most common possibilities include the use of spreadsheets, or even building models, to identify savings for each energy-saving activity.
Both alternative strategies may require more work, as alternative energy contractors may have adopted several strategies during their tenure.
Another drawback of the spreadsheet is that the energy-saving strategy may interact with each other, so the total savings may not be the sum of the different strategies, and finally, the spreadsheet is usually the prediction of energy-saving, not the measurement. 2)
The cooling days are defined in detail later in this chapter, but here gives a rough meaning.
Cooling degrees days roughly measure the impact of a period of weather on building cooling requirements.
A hotter day can lead to more cooling days, while a colder day may not have a cooling day.
Twice the number of cooling days should result in twice the building cooling requirements.
Calculate the number of cooling days separately per day.
The number of cooling days in a month or billing period is only the sum of the number of cooling days for a single number of days.
The same is true for heating days. 3)
Some energy professionals choose bills for 2 years instead of 1 year.
There are good reasons to choose a year or two.
Do not select a period of 12 months apart (
For example, 15 months or 8 months may result in inaccuracies). 4)
Please do not confuse the base year with the baseline.
The base year is a time period from which bills are used to determine the energy usage patterns of buildings relative to weather data, whereas the baseline, as described later, indicates how much energy we will use this month according to the basic year energy usage model and current month conditions (i. e.
Weather and days in the bill). 5)
This step can be tedious in a spreadsheet. 6)
The statistical calculation behind the R2 value, and the processing of the other three useful indicators, T-
Statistics, mean deviation errors, and CVRMSE are not handled in this chapter.
For more information on these statistical concepts, please refer to any university statistics textbook. (
For energy contractors, R2 values and T-
Statistics are usually enough. )7)
Baseline equation = fitted linear equation /-
Baseline modifications.
We will introduce baseline modifications later in this chapter. 8)
You will not sum or average over a period of time for high or low temperatures, as the results will not be useful.
However, you can sum the number of degrees days and the results are still useful because it is proportional to the heating or cooling requirements of the building. 9)
If you think about it, you don\'t need to deal with this on the architectural level, but you can look at it on the 1 m level. (
To simplify the presentation, we\'re looking at it from a architectural point of view because it\'s not very abstract. )
Some buildings have many meters, some of which may be related to different central plants.
In this building, different central plants may have different equilibrium points, because the conditions associated with different parts of the building may be different. 10)
If you calculate the number of degree days by hand or spreadsheet, you will use the following formula in your calculation.
Of course, commercially available utility manager software that performs weather normalization will automatically handle this issue.
Daily HDDi = [TBP – (Thi + Tlo )/ 2 ]
CDDi = [1 day](Thi + Tlo )/ 2 – TBP ]
X 1 day, where: HDDi = heating days of the day cddi = heating days of the day tbp = equilibrium temperature, thi = daily high temperature tlo = daily low temperature means you will never have a negative living.
If a negative number is generated by HDDi or CDDi calculations, the result for that day is 0 degrees days.
Heating and cooling days can be summed in a few days, one month, one billing period, one year, or any interval greater than one day, respectively.
For the billing period (
Or any period of more than one day)
Hard disk = sigma HDDiCDD Sigma CDDiTake looking back for a month.
3, you may have noticed more than twice the number of cooling days (CDD)
August 2005 is over August 2004.
Because the number of cooling days is roughly proportional to the cooling energy usage of the building, one can correctly assume that the cooling demand of the building will also roughly double.
Customers often want to know that they have saved energy and costs as promised.
From the customer\'s point of view, the simplest and easiest to understand proof of energy conservation comes from a simple comparison of electricity charges.
Does the system save electricity? (1)
In theory, a simple comparison of pre-
Installation bill mailed
Install the bill and you will see if you have saved it.
But if it\'s so easy, why write a paper on it?
It\'s not easy.
Let\'s find out why. Figure 1.
1: expected before and after
Transformation and use of chilled water system. (
Http: suppose a solar contractor installed a new solar system for a building.
One may expect to save energy and costs from this transformation. Figure 1.
1 present the results that our alternative energy contractors may expect.
But what if the bill shows the disaster shown in Figure 1. 2? Figure 1.
2. Disaster of a project?
Comparison of Pre-
Transformation and postTransformation data (
Http: Imagine showing customers these results after they have invested hundreds of billions of dollars in your system.
Results like this can hardly inspire confidence in your abilities.
How should solar contractors present this data to their customers?
Do you think the contractors are confident about the work and the recommendation for future solar projects? Probably not.
Customers may simply look at the numbers because they don\'t lie, so they come to the conclusion that they hired the wrong contractor and the solar system didn\'t work very well!
There are many reasons why the system may not achieve the expected savings.
One possibility is that the project is saving money, but the summer after installation is much hotter than the summer before installation.
Hot summer can lead to higher air conditioning loads, which can lead to higher utility charges.
In our case, the hot summer> higher air conditioning load> higher summer utility costs, which we claim is because
It\'s hotter to install, and although the solar power project does, it doesn\'t look like it saves any energy.
Imagine explaining this to the customer!
If the weather is really the reason for the increase in usage, then how do you use utility bills to measure savings on solar projects?
The number of your savings will depend on the weather.
There is no value in savings numbers (
Unless the weather is the same year after year).
Our example may seem a bit exaggerated, but it raises the question: will weather really have such a big impact on savings numbers?
It\'s OK, but it\'s not that extreme usually.
The summer of 2005 is the hottest summer of the century.
Stay in Detroit, Michigan
Compared to the usual 12 days, there are 18 days of 90 degrees Fahrenheit or above.
In addition, the average temperature in Detroit is 74.
8 °F compared to normal 71. 4 °F.
At first glance, 3 degrees doesn\'t seem to matter, however, if you convert the temperature to the number of days of cooling (2)
As shown in Figure 1.
The result looks dramatic.
Compared with June to August, there were 909 cooling days in 2005, compared with 442 cooling days in 2004.
This is more than double!
The number of cooling days is roughly proportional to the relative building cooling requirements.
So, for Detroit, one can infer the average building needed (
And may be consumed)
The cooling energy in the summer of 2005 is more than twice that in the summer of 2004.
It is likely that there are several solar contractors in the Midwest facing this problem! Figure 1.
3: cooling days of 2004 and 2005 in Detroit, Michigan (
Http: In this case, how does the solar contractor demonstrate the savings of the solar power system?
Simply comparing utility bills won\'t work because the expected savings will be covered up by the increased cold load.
The solution is to apply the same weather data in some wayand post-
Install the bill.
Then extreme weather will not be punished.
This is the role of weather normalization.
Display savings after renovation (
Or good practices for alternative energy)
To avoid our catastrophic example, alternative energy contractors should normalize the cost of weather utilities so that changes in weather conditions do not compromise savings figures.
With more and more energy managers, energy engineers, the practice of normalizing energy bills with energy software is becoming popular, and contractors correct bills for weather reasons, because they want to prove that they are actually trying to save energy.
There are many names for this process: weather correction, normalization of weather, adjustment of weather or return of weather.
When we use weather normalization, how does weather normalization work, instead of comparing last year\'s usage with this year\'s usage, let\'s compare how much energy we will use this year, how much energy we use this year.
Many people in our industry do not think that the result of this comparison is \"saving\", but \"Avoiding use\" or \"avoiding costs \"(
If compare cost).
But since we are trying to keep this chapter at the introductory level, we will simply use the word \"save.
When we tried to compare last year\'s usage to this year\'s usage, we saw Figure 1.
A disastrous project.
We used the equation: Savings = usage for last year-when we normalized the weather, usage for this year was the same as the data in figure 1.
4, and use the equation: Save = how much energy we will use this year-use Figure 1 for this year.
4. Comparison between baseline and actual (Post-Retrofit)
Data on weather correction (
Http: The next question is, how do we calculate how much energy we will use this year?
This is why the weather is normalized.
First of all, we choose the utility bill for one year (3)
We want to compare future usage.
This is usually the year before you start an alternative energy project, the year before the installation and renovation, or the year before you, the new energy contractor, were hired, or just the past year when you want to compare current usage.
In this example, we will select the year of utility data before installing the solar power system.
We will call this year the foundation year (4). Figure 1.
5: Days of cooling (
Http: then we calculate the number of degree days for the base year billing period.
Since this example only involves cooling, we only need to collect the number of days of cooling (
Days without heating).
There is a section later in this chapter about the calculation of the number of degree days.
At present, it is only necessary to recognize that the number of cooling days need to be collected at this step. (5)Figure 1.
Cooling days for 5 years. Figure 1.
6. Find the relationship between usage and weather data.
The Blue Point represents utilities.
The red line is the most suitable line. (
Http: to establish the relationship between usage and weather, we found the line closest to all the bills.
This line is the most suitable one, found using statistical regression techniques in canned utility bill tracking software and spreadsheets.
The next step is to ensure that the best fit line is good enough to use.
The quality of the best fitting line is represented by statistical indicators, the most common of which is the R2 value.
The R2 value represents the goodness of fit, R2> 0 in the field of energy engineering.
75 is considered acceptable.
Some meters have little or no sensitivity to the weather, or may have other unknown variables that have a greater impact on use than the weather.
The R2 value of these meters may be very low.
You can generate R2 values for fit line in Excel or other canned utility bill tracking software. (6)
There is an equation for this optimal fitting line, which we call a fitting line equation, or in this case, a baseline equation. (7)
Fitting line equation in figure 1.
6 may be: baseline kWh = (
5 KW hours/day * days)+ (
417 kw h/CDD * CDD)
Once we have this equation, we complete the regression process.
Let\'s review what we did: We standardized weather data for basic year utility bills and days in our bills.
We drew standardized benchmark annual utility data and standardized weather data.
We found the most suitable line through the data.
The most appropriate line then represents the utility bill for the benchmark year.
The best fit line equation represents the best fit line, while the best fit line represents the base year of utility data.
Benchmark year Bill = best fit line = fitting line equation represents how the customer uses energy in the benchmark year and will continue to use it in the future (
Respond to changing weather conditions)
It is assumed that there is no major change in the consumption pattern of buildings.
Once you have the baseline equation, you can determine if you save any energy. How?
You collect the bill from a certain billing period after the base year. You (
Or your software)
Insert the days of the bill and the cooling days of the billing period into the baseline equation.
Assuming that there are 30 days of billing for the current month, the CDD associated with the billing period is 100. Benchmark = (
5 KW hours/day * days)+ (
417 kw h/CDD * CDD)Benchmark = (5 kWh/Day * 30)+ (
417 KW/CDD * 100)
Baseline kWh = 41,850 kWhRemember, the baseline equation indicates how the customer uses energy during the base year.
Therefore, with the new input of days and degree days, the baseline equation will tell you how much energy the building will use this year based on the base year usage pattern and this year\'s situation (
Weather and days).
We call this usage determined by the baseline equation, the baseline use.
Now, to get a fair estimate of energy savings, we compare: saving = how much energy we will use this year-how much energy we do use this year, or if we change the terms a little: savings = baseline energy use-actual energy use, where baseline energy use is calculated by the baseline equation, using the weather and days of the month, and the actual energy use is the bill for the month.
The first two equations are the same, with baseline = \"how much energy we will use this year\", which actually represents \"how much energy we do use this year \".
So, in our case, let\'s say the bill for this month is 30,000 KW: savings = baseline energy use-actual energy use = 41,850 KW hours-30,000 KW hours savings = 11,850 KW hours calculate the degree day and find the balance point cool degree day (CDD)
Roughly proportional to the energy used to cool the building, and the number of days of heating ,(HDD)
Roughly proportional to the energy used to heat a building.
Although the number of degree days is simple to calculate, it is very useful in the energy meter calculation.
They are calculated for each day and then summed for a period of time (
A month, a year, etc. ). (8)Figure 1.
7: determine the balance point using kWh/day
Outdoor temperature chart (
Http: In general, the number of degree days per day is the difference between the building balance point and the average outdoor temperature.
So, to understand living, we need to understand the concept of balance point first.
Buildings have their own set of equilibrium points for heating and cooling-they may not be the same.
The heating equilibrium point can be defined as the outdoor temperature at which the building starts heating.
In other words, when the outdoor temperature drops below the heating balance point, the heating system of the building will start.
Instead, the building starts to cool when the outdoor temperature is higher than the cooling balance point. (9)
The balance point of the building is determined by almost everything associated with it, because almost every component associated with the building has a certain impact on the heating of the building: building envelope (
Insulation values, shadows, windows, etc. )
, Temperature setting point, thermostat setting schedule (if any), the number of devices that generate heat (and people)
Lighting intensity, ventilation, HVAC system type, HVAC system Schedule, lighting and miscellaneous equipment schedule in the building, among other factors.
In the past, before energy professionals used computer and utility manager software in their daily tasks, degree day analysis was simplified by assuming that the balance point of heating and cooling was 65 degrees Fahrenheit.
Therefore, it is easy to publish and distribute degree days as everyone calculates degree days using the same criteria (
That is, the balance point is 65 °F).
However, it is more accurate to recognize that each building has its own balance point and calculate the number of degree days accordingly.
Therefore, it is unlikely that you will see the number of degree days available, as more complex analysis requires you to calculate your degree days based on the balance point of your own building. (10)Figure 1.
8: average temperature between kWh/day and outdoor (
Http: to find the equilibrium temperature of the building, please compare the usage/day to the average outdoor temperature (
Billing cycle)
As shown in Figure 1. 7.
Please note that figure 1.
There are two trends.
One trend is flat and the other is tilted up and right.
We draw a red line that represents two trends in figure 1. 8. (
Ignore the vertical red line for the time being. )
Flat trend represents Non
Temperature-sensitive consumption is an electrical consumption that is not related to the weather. In Figure 1. 7, Non-
The temperature consumption is roughly the same every month, about 2450 degrees per day.
Example of non
Temperature-sensitive consumption includes lighting, computers, various plug loads, industrial equipment and well pumps.
Any use above the horizontal red line is called temperature sensitive consumption, representing electrical use related to the building cooling system.
Note that in figure 1.
8, temperature sensitive consumption occurs only at temperatures greater than 61 °F.
The intersection of these two trends is called the equilibrium point, that is, the equilibrium point temperature, 61 °F in this case.
Note that in figure 1.
8. Consumption increases with the increase of outdoor temperature.
The building uses more energy as the outdoor temperature rises, so the meter is used for cooling instead of heating.
The equilibrium temperature we find is the cooling equilibrium temperature (
Not the temperature of the heating equilibrium point). Figure 1.
9: average temperature of kWh/day and outdoor (
Http: for heating use in figure 1, we can view the same type of chart. 9.
Note that the main difference between the two charts is that the temperature sensitive trend is tilted up and left (
Not up and right).
As the outdoor temperature drops, the building uses more power to heat the building.
Now that we have determined the temperature of the equilibrium point, we have all the information we need to calculate the number of days of the degree.
If your chart is similar to Figure 1
9, you will use the number of days of heating.
If your chart is similar to Figure 1.
8. You will use the cooling days. Figure 1.
10: normal daily use of production and weather.
The baseline equation is shown at the bottom of the graph (
Http: normal other VARIABLESMore and energy experts start to understand the value specification practical data production (or instead of)weather.
This works if you have a simple variable to quantify your production.
For example, a computer assembly plant can track the number of computers produced.
If a factory produces several different products, such as disk drives, desktop computers and printers, it may be difficult to come up with a single variable to represent the production of the entire factory (i. e.
Tons of products).
However, from the analysis is done on a level of 1 m, not on a level, if you have meters (or submeters)
This only serves a production line, then you can standardize the use of the products produced with that line from 1 m. Figure 1.
10 shows the standardized daily use and production of the widget factory.
The normalized baseline equation is shown at the bottom of the graph.
Notify the production unit (UPr)
And cooling days (CDD)
This means that this normalization includes weather data and production data.
School districts, colleges and universities usually normalize their school calendars.
Property issues, normal stay at Hotel and prison.
Basically, any variable can be used for normalization as long as it is an accurate, consistent predictor of the energy usage pattern.
Again, these normalization can be performed through dedicated utility bill tracking software or using spreadsheets.
The weather is different every year.
So it\'s hard to know if the change in your utility bill is due to weather fluctuations, whether it\'s due to your alternative energy system, or both.
If you want to use utility bills to determine how much energy-efficient an alternative energy system is, it is critical to remove weather variability from your energy-saving equation.
This is done using the weather normalization technique described in this article.
You can also adjust the use of other variables, such as occupancy or production. --1)
What is the alternative?
The most common possibilities include the use of spreadsheets, or even building models, to identify savings for each energy-saving activity.
Both alternative strategies may require more work, as alternative energy contractors may have adopted several strategies during their tenure.
Another drawback of the spreadsheet is that the energy-saving strategy may interact with each other, so the total savings may not be the sum of the different strategies, and finally, the spreadsheet is usually the prediction of energy-saving, not the measurement. 2)
The cooling days are defined in detail later in this chapter, but here gives a rough meaning.
Cooling degrees days roughly measure the impact of a period of weather on building cooling requirements.
A hotter day can lead to more cooling days, while a colder day may not have a cooling day.
Twice the number of cooling days should result in twice the building cooling requirements.
Calculate the number of cooling days separately per day.
The number of cooling days in a month or billing period is only the sum of the number of cooling days for a single number of days.
The same is true for heating days. 3)
Some energy professionals choose bills for 2 years instead of 1 year.
There are good reasons to choose a year or two.
Do not select a period of 12 months apart (
For example, 15 months or 8 months may result in inaccuracies). 4)
Please do not confuse the base year with the baseline.
The base year is a time period from which bills are used to determine the energy usage patterns of buildings relative to weather data, whereas the baseline, as described later, indicates how much energy we will use this month according to the basic year energy usage model and current month conditions (i. e.
Weather and days in the bill). 5)
This step can be tedious in a spreadsheet. 6)
The statistical calculation behind the R2 value, and the processing of the other three useful indicators, T-
Statistics, mean deviation errors, and CVRMSE are not handled in this chapter.
For more information on these statistical concepts, please refer to any university statistics textbook. (
For energy contractors, R2 values and T-
Statistics are usually enough. )7)
Baseline equation = fitted linear equation /-
Baseline modifications.
We will introduce baseline modifications later in this chapter. 8)
You will not sum or average over a period of time for high or low temperatures, as the results will not be useful.
However, you can sum the number of degrees days and the results are still useful because it is proportional to the heating or cooling requirements of the building. 9)
If you think about it, you don\'t need to deal with this on the architectural level, but you can look at it on the 1 m level. (
To simplify the presentation, we\'re looking at it from a architectural point of view because it\'s not very abstract. )
Some buildings have many meters, some of which may be related to different central plants.
In this building, different central plants may have different equilibrium points, because the conditions associated with different parts of the building may be different. 10)
If you calculate the number of degree days by hand or spreadsheet, you will use the following formula in your calculation.
Of course, commercially available utility manager software that performs weather normalization will automatically handle this issue.
Daily HDDi = [TBP – (Thi + Tlo )/ 2 ]
CDDi = [1 day](Thi + Tlo )/ 2 – TBP ]
X 1 day, where: HDDi = heating days of the day cddi = heating days of the day tbp = equilibrium temperature, thi = daily high temperature tlo = daily low temperature means you will never have a negative living.
If a negative number is generated by HDDi or CDDi calculations, the result for that day is 0 degrees days.
Heating and cooling days can be summed in a few days, one month, one billing period, one year, or any interval greater than one day, respectively.
For the billing period (
Or any period of more than one day)
Hard disk = sigma HDDiCDD Sigma CDDiTake looking back for a month.
3, you may have noticed more than twice the number of cooling days (CDD)
August 2005 is over August 2004.
Because the number of cooling days is roughly proportional to the cooling energy usage of the building, one can correctly assume that the cooling demand of the building will also roughly double.
Comments
Post a Comment