thesis about electricity consumption

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A Comprehensive Study of Electricity Consumption of People in Each Region in the Philippines

2019, American Journal of Mechanics and Applications

Related Papers

Elizabeth Remedio

Eddie S. See , Mary Ann M. See

International Social Science Journal

RONALDO CABAUATAN

Energy for Sustainable Development

Muhammad Arif Wijaya

JIET (Jurnal Ilmu Ekonomi Terapan)

Jurnal Ilmu Ekonomi Terapan Universitas Airlangga

Electrical energy is one energy source that plays an essential role in human life daily, such as industrial, commercial, government, and household activities. All processes related to public activities can dash effectively and efficiently with electricity. The electricity consumption in Indonesia has reportedly increased every year. This enhancement can be caused by several factors, one of which is the population. The increasing electricity consumption in Indonesia has shown that electricity is the primary driving sector for the development that supports productivity and public activities. It is hoped that the economy will also increase. Based on this statement, this study aimed to analyze the factors which affect electricity consumption in Indonesia during the 2015-2019 period. This study used secondary data by taking four independent variables, including GDRP per capita, population, installed power capacity, and electricity tariffs. The dependent variable used in this study is electricity consumption. The research used the estimation technique of the Fixed Effect Model, which was selected based on the result of the Chow Test. The results in the regression analysis showed that the GDRP per capita and population variable both resulted in positive and insignificant effects on the electricity consumption in Indonesia during the 2015-2019 period. The installed power capacity variable had a positive and significant influence on the electricity consumption in Indonesia during the 2015-2019 period. Meanwhile, the electricity rate variable had a negative and insignificant effect on the electricity consumption in Indonesia during the 2015-2019 period.

International Journal of Social and Local Economic Governance

ghozali maski

asmarashid ponniran

Sustainability

Electricity-saving strategies are an essential solution to overcoming increasing global CO2 emission and electricity consumption problems; therefore, the determinant factors of electricity consumption in households need to be assessed. Most previous studies were conducted in developed countries of subtropical regions that had different household characteristic factors from those in developing countries of tropical regions. A field survey was conducted on electricity consumption for Malaysian households to investigate the factors affecting electricity consumption that focused on technology perspective (building and appliance characteristics) and socio-economic perspective (socio-demographics and occupant behaviour). To analyse the determinant factors of electricity consumption, direct and indirect questionnaire surveys were conducted from November 2017 to January 2018 among 214 university students. Direct questionnaire surveys were performed in order to obtain general information tha...

The Online Journal of Science and Technology

Enock Duodu

The study assessed the efficient utilization of electricity by domestic consumers in the Agona District. Descriptive survey design was employed in the study. Purposive and simple random sampling techniques were used in selecting five (5) towns and 100 respondents, respectively. A questionnaire as well as interview and observation methods were used in data collection. The data obtained from respondents were analyzed using frequencies and percentages. The study revealed that almost two-thirds (63%) of the respondents in the condominium consume electricity from a single central credit meters. Again, the study showed that lack of access to energy efficient technologies have contributed to the waste of electricity in the households. The results also revealed that consumers have little or no knowledge about some basic energy conservation tips. It is recommended that all households in such condominium should be provided with separate meters preferably the pre-paid meters so as to encourag...

Sekiguchi Global Research Association Forum in Waseda University, Tokyo - Seminar Proceedings

Stephanie N Gilles

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Methods of forecasting electric energy consumption: a literature review.

1. Introduction

Conducted a comparative analysis of the methods by determining the main advantages and disadvantages, the scope, and specific features of the most popular methods used in forecasting electricity consumption.
Conducted a comparative analysis of the accuracy of existing forecasting methods using the magnitude of the average absolute error in percent (MAPE).
Conducted a comparative analysis of forecasting methods by the complexity of the implementation of the methods under consideration based on the level of requirements for computational capabilities and the complexity of reproducing the method.
Conducted a comparative analysis of the methods of the requirements for the initial data set required for the application of the method.
Identified the most promising and productive methods.

2. Methods and Materials

3. comparative analysis of forecasting methods, 3.1. operative forecasting, 3.2. short-term forecasting, 3.3. medium-term forecasting, 3.4. long-term forecasting, 4. conclusions, author contributions, data availability statement, conflicts of interest.

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Method	Scope of Application	Forecasting Error		Identified Problems	Advantages of the Method	The Possibility of Using the Model to Solve Short-Term Forecasting Problems (i.e., How it Will Behave for Long Intervals)	Main Publications	Initial Dataset	Performance (Forecast Execution Time, s)
Method	Scope of Application	%	Estimation (+/−)	Identified Problems	Advantages of the Method		Main Publications	Initial Dataset	Performance (Forecast Execution Time, s)
Perceptron	Forecasting of power consumption in regional control rooms, the building of justice in the USA	1.25 (standard error per day) 3.9	+	Overfitting; problems of obtaining detailed data; complexity of interpretation of results.	The random component is taken into account due to the presence of an adaptation contour, with the help of which deviations in the model are taken into account.	The model is applicable to the problem of short-term forecasting provided that previous temperature observations are taken into account.	[ , ]	1. The actual load of the system at time t. 2. Previous load observations (t-n). 5760 fifteen-second data values of all input data for each type of day during a 90-day data period: heating, ventilation and air conditioning kW, type of day (weekend/working), time of day, external temperature, humidity.	No information available
LSTM network using the “Butterfly” optimization algorithm	Private households	9.5 (tested on two datasets)	+	The complexity of implementation, the need to take into account a large number of factors.	Productive (in terms of forecast execution time) compared to other models (ensemble-based deep-learning model, CNN with GRU model, multilayer bidirectional GRU with CNN) due to the use of the algorithm for selecting optimal hyperparameters of the Butterfly network. Tested on publicly available datasets. LSTM networks have a memory mechanism and effectively cope with the problem of gradient attenuation.	More research is needed	[ ]	Detailed data on power consumption: for the first data set, 29 parameters related to the energy consumption of appliances, lighting and weather information (pressure, temperature, dew point, humidity, wind speed, etc.); for the second data set, the original data set contains nine attributes, such as voltage, minutes, global intensity, month, global active power, year, global reactive power, day, and hour. Three more variables are obtained from sensors.	12 and 13 for two datasets
Quantile Regression Fast Forest	Mining industry: Ben Guerir Quarry, Morocco	1%	+	Additional research of the model is needed for the possibility of real-time application.	Good performance of the method	The model is applicable for operational and short-term forecasting.	[ ]	The model is designed to function in SCADA systems as an intelligent module. It is possible to connect alternative energy sources.	10
Autoregressions of the integrated moving average ARIMA	The building of justice in the USA	6.9	+	The need to re-evaluate the model when adding new data to the model, the low generalizing ability of the algorithm, the complexity of parameter selection.	A well-developed mathematical apparatus, a formalized method of checking the model for adequacy to experimental data	More research is needed. High probability of applicability of the method for the problem of short-term forecasting	[ ]	5760 15-second data values of all input data for each type of day during a 90-day data period: heating, ventilation and air conditioning kW, type of day (output/working), time of day, external temperature, humidity.	No information available
Linear regression	The building of justice in the USA	15.2	−	Inefficient in the case of complex dependencies in the source data, unstable to outliers and omissions in the data	Ease of implementation and interpretation of results	More research is needed	[ ]		No information available

Method	Scope of Application	Forecasting Error		Identified Problems	Advantages of the Method	The Possibility of Using the Model to Solve Problems of Medium-Term Forecasting (i.e., How It Will Behave for Long Intervals)	Main Publications	Initial Dataset
Method	Scope of Application	%	Estimation (+/−)	Identified Problems	Advantages of the Method		Main Publications	Initial Dataset
Perceptron	Regional Dispatch Management	Weekly average 1.82 1.46	+	The need to retrain the network to account for the seasonality of data. Retraining the network.	The possibility of obtaining a reliable forecast in conditions of incompleteness of the initial data, a small complexity of the method.	More research is needed.	[ , ]	Hourly load values for the days preceding the predicted ones (24 values) and for the days a week ago (24 values). Initial network training: load data from two weeks ago. Accounting for the type of day (working, weekend, or holiday).
Fuzzy neural network	Regional Dispatch Management	Mean for a week 1.18 1.34	+	The complexity of the method, the need for an expert decision when choosing the necessary rules. The need for experimental selection of parameters.	The ability to add new rules.	It can be used for medium-term forecasting (for a month in advance) with a decrease in the accuracy of forecasting and, consequently, the need to re-evaluate the parameters of the model.	[ , ]	Hourly load values for the days preceding the predicted ones (24 values) and for the days a week ago (24 values). Initial network training: load data from two weeks ago. Accounting for the type of day (working, weekend, or holiday). Consideration of meteorological factors.
Hybrid model: multidimensional singular spectral analysis and fuzzy neural network	Regional Dispatch Management	1.3	+	The greater complexity of the method compared to the isolated use of a fuzzy neural network	The possibility of decomposing a number of power consumption into additive components: trend, seasonal (harmonic), and noise, which contributes to a better selection of signs supplied to the input of the neural network.	It can be used for medium-term forecasting (a month in advance) with a decrease in the accuracy of forecasting and, consequently, the need to re-evaluate the parameters of the model.	[ ]	Daily schedules of electrical loads, meteorological factors (temperature, natural light)
One-dimensional Convolutional Neural Network (CNN)	Sewage treatment plants	<5% (630 kWh standard error of the forecast)	+	The accuracy of the forecast can be higher if one adds data on the level of water pollution. However, there is a delay in obtaining the initial data: data on water level pollution is obtained through laboratory means, so the forecast of electricity consumption with these factors can be obtained only after a day.	Shorter forecast execution time and better generalizing ability of the method compared to the perceptron.	Additional research is needed.	[ ]	Daily data on energy consumption. Data characterizing the volume of water flow at the entrance to treatment facilities and meteorological factors (25 factors, including temperature, humidity, wind speed, etc.). Open data of the wholesale electricity market and the total planned consumption capacity for the second price zone (zone Siberia).
Holt–Winters exponential smoothing model using a moving average to identify trends when smoothing graphs	Enterprises of the mineral resource complex located on the territory of Siberia	1.08	+	The complexity of the method. The complexity of the selection of model parameters.	Taking into account the trend and seasonality contributes to obtaining an accurate forecast. The model gives more accurate prediction results than the neural network model.		[ , ]
Recurrent neural network LSTM	Power systems of large federal districts of the Russian Federation	2.1	+	The complexity of the selection of model parameters. The difficulty of interpreting the forecast	Fast execution of the forecast. The presence of a memorization mechanism. Taking into account the nonlinearity of the source data. Lower susceptibility to outliers compared to classical methods (moving average, linear regression).		[ ]	Data on the power consumption of federal districts for 13 years, meteorological data, information about the day of the week according to the production calendar, and features of industrial production in the relevant federal district (statistical information). Data for 3 days preceding the forecast period are sufficient for forecasting.
Linear regression	Power systems of large federal districts of the Russian Federation	3.3	+	Susceptibility to seasonal fluctuations. The need to retrain the model when using new data.	Easy to implement		[ ]

Method	Scope of Application	Forecasting Error		Identified Problems	Advantages of the Method	The Possibility of Using the Model to Solve Short-Term/Long-Term Forecasting Problems (i.e., How It Will Behave for Sort Intervals)	Main Publications	Initial Dataset	Performance (Forecast Execution Time, s)
Method	Scope of Application	%	Estimation (+/−)	Identified Problems	Advantages of the Method		Main Publications	Initial Dataset	Performance (Forecast Execution Time, s)
Perceptron	Large residential buildings in 6 districts of China	13.29 (average for 6 buildings)	+	Network overfitting. The complexity of the method implementation. The complexity of obtaining the source data.	The principal component method and multiple regression analysis are used to analyze factors, which increases the accuracy of the model. The Levenberg–Marquardt algorithm was chosen as the neural network learning algorithm. There is an analysis of the application of the method by season: the best forecast in winter (error 4.51%), the worst in winter (8.82%).	The model is applicable to solving hourly forecasting problems.	[ , ]	Load on the power grid, social factors (employment): twelve input data: month, day, hour, minute, outdoor air temperature, outdoor air humidity, outdoor air pressure, wind speed, wind direction, visibility, number of people under the age of fifteen (for example, zero, one, two, three and etc.), and current power consumption	No information available
CatBoost Gradient Boosting	A small industrial enterprise	7.95%	+	The model does not take into account the data of the technological process.	The performance of the model. Automated selection of hyperparameters. Resistance to emissions.	The model can be used for short-term forecasting. Additional studies are needed to evaluate the application in the long term.	[ ]	Monthly power consumption for 5 years by divisions of the enterprise with a division into technical needs and lighting. Monthly average weather data (humidity, wind speed, temperature, dew point temperature).	50
Autoregression of the integrated moving average ARIMA	A small industrial enterprise	15.92	−	Insufficient level of model accuracy due to the complexity of the selection of model parameters.	A well-developed mathematical apparatus. Ease of implementation.	More research is needed.	[ ]	Monthly power consumption for 5 years by divisions of the enterprise with a division into technical needs and lighting. Monthly average weather data (humidity, wind speed, temperature, dew point temperature).	70

Method	Scope of Application	Forecasting Error		Identified Problems	Advantages of the Method	The Possibility of Using the Model to Solve Problems of Medium-Term Forecasting	Main Publications	Initial Dataset	Performance (Forecast Execution Time, s)
Method	Scope of Application	%	Estimation (+/−)	Identified Problems	Advantages of the Method		Main Publications	Initial Dataset	Performance (Forecast Execution Time, s)
A method based on the values of rank norms	Socioeconomic systems of the regions of the Russian Federation	4.87	+	The complexity of the mathematical apparatus. The need for competencies in the field of rank analysis of technocenoses.	Obtaining a reliable forecast without the need to take into account factors affecting power consumption.	The model can be applied for medium-term forecasting.	[ ]	Monthly data on regional electricity consumption Russia for eleven years from 2009 to 2019	No information available
Deep Neural Network with Fuzzy Wavelets	Urban Buildings	−	+	The complexity of the mathematical apparatus. The complexity of evaluating the results of forecasting.	A small number of rules and less complexity of the model compared to a fully connected neural network.	The model can be applied for medium-term forecasting.	[ ]	Electricity consumption in Tehran, Mashhad, Ahvaz, and Urmia from 2010 to 2021	50
Delphi	The Arctic Energy System	−	+	Subjectivity of expert opinions. The complexity of evaluating the results of forecasting.	There are three reasonable scenarios of development: positive, negative, and neutral. Taking into account many risks when developing scenarios. Analysis of consumers by energy consumption level.	More research is needed.	[ ]	Expert assessments	No information available
Gray Model	China’s Industries	−	+	The complexity of evaluating the results of forecasting.	A small amount of the initial sample is necessary for forecasting. Consideration of socio-economic development scenarios.	Not applicable	[ ]	Data on the level of production, urbanization and socioeconomic development for 2011–2020	No information available
Nonlinear autoregressive exogenous neural network	Regional municipalities	4.2	+	The complexity of selecting hyperparameters of the model	Prediction accuracy. The possibility of using the method for other prediction intervals. The ability to scale the model taking into account new factors.	It is possible to build a forecast for the short and medium term.	[ ]	The dataset includes hourly load demand data for nine years for Bruce County, Ontario, Canada combined with climate information (temperature and wind speed) for 2010–2018 (forecast made for 2019).	960

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Klyuev, R.V.; Morgoev, I.D.; Morgoeva, A.D.; Gavrina, O.A.; Martyushev, N.V.; Efremenkov, E.A.; Mengxu, Q. Methods of Forecasting Electric Energy Consumption: A Literature Review. Energies 2022 , 15 , 8919. https://doi.org/10.3390/en15238919

Klyuev RV, Morgoev ID, Morgoeva AD, Gavrina OA, Martyushev NV, Efremenkov EA, Mengxu Q. Methods of Forecasting Electric Energy Consumption: A Literature Review. Energies . 2022; 15(23):8919. https://doi.org/10.3390/en15238919

Klyuev, Roman V., Irbek D. Morgoev, Angelika D. Morgoeva, Oksana A. Gavrina, Nikita V. Martyushev, Egor A. Efremenkov, and Qi Mengxu. 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review" Energies 15, no. 23: 8919. https://doi.org/10.3390/en15238919

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Energy Production and Consumption

Explore data on how energy production and use varies across the world..

The availability of energy has transformed the course of humanity over the last few centuries. Not only have new sources of energy been unlocked — first fossil fuels, followed by diversification to nuclear, hydropower, and now other renewable technologies — but also in the quantity we can produce and consume.

This article focuses on the quantity of energy we consume — looking at total energy and electricity consumption; how countries compare when we look at this per person; and how energy consumption is changing over time.

In our pages on the Energy Mix and Electricity Mix , we look in more detail at what sources provide this energy.

Global energy consumption

How much energy does the world consume.

The energy system has transformed dramatically since the Industrial Revolution. We see this transformation of the global energy supply in the interactive chart shown here. It graphs global energy consumption from 1800 onwards.

It is based on historical estimates of primary energy consumption from Vaclav Smil, combined with updated figures from BP's Statistical Review of World Energy . 1

Note that this data presents primary energy consumption via the “substitution method”. The substitution method — in comparison to the direct method — attempts to correct for the inefficiencies (energy wasted as heat during combustion) in fossil fuel and biomass conversion. It does this by correcting nuclear and modern renewable technologies to their “primary input equivalents” if the same quantity of energy was produced from fossil fuels.

How is global energy consumption changing year-to-year?

Demand for energy is growing across many countries in the world, as people get richer and populations increase.

If this increased demand is not offset by improvements in energy efficiency elsewhere, then our global energy consumption will continue to grow year-on-year. Growing energy consumption makes the challenge of transitioning our energy systems away from fossil fuels towards low-carbon sources of energy more difficult: new low-carbon energy has to meet this additional demand and try to displace existing fossil fuels in the energy mix.

This interactive chart shows how global energy consumption has been changing from year to year. The change is given as a percentage of consumption in the previous year.

We see that global energy consumption has increased nearly every year for more than half a century. The exceptions to this are in the early 1980s, and 2009 following the financial crisis.

Global energy consumption continues to grow, but it does seem to be slowing — averaging around 1% to 2% per year.

Primary energy consumption

Total energy consumption.

How much energy do countries across the world consume?

This interactive chart shows primary energy consumption country-by-country. It is the sum of total energy consumption, including electricity, transport, and heating. We look at electricity consumption individually later in this article.

Note, again, that this is based on primary energy via the substitution method: this means nuclear and renewable energy technologies have been converted into their “primary input equivalents” if they had the same levels of inefficiency as fossil fuel conversion.

Per capita: where do people consume the most energy ?

When we look at total energy consumption, differences across countries often reflect differences in population size: countries with lots of people inevitably consume more energy than tiny countries.

How do countries compare when we look at energy consumption per person ?

This interactive chart shows per capita energy consumption. We see vast differences across the world.

The largest energy consumers include Iceland, Norway, Canada, the United States, and wealthy nations in the Middle East such as Oman, Saudi Arabia, and Qatar. The average person in these countries consumes as much as 100 times more than those in some of the poorest countries.

In fact, the true differences between the richest and poorest might be even greater. We do not have high-quality data on energy consumption for many of the world's poorest countries. This is because they often use very few commercially traded energy sources (such as coal, oil, gas, or grid electricity) and instead rely on traditional biomass — crop residues, wood, and other organic matter that is difficult to quantify. This means we often lack good data on energy consumption for the world's poorest.

Where is energy consumption growing or falling?

Year-on-year change in primary energy consumption.

Globally, primary energy consumption has increased nearly every year for at least half a century. But this is not the case everywhere in the world.

Energy consumption is rising in many countries where incomes are rising quickly and the population is growing. But in many countries — particularly richer countries trying to improve energy efficiency — energy consumption is actually falling.

This interactive chart shows the annual growth rate of energy consumption. Positive values indicate a country's energy consumption was higher than the previous year. Negative values indicate its energy consumption was lower than the previous year.

Electricity generation

Total electricity generation: how much electricity does each country generate.

We previously looked at total energy consumption. This is the sum of energy used for electricity, transport, and heating.

Although the terms “electricity” and “energy” are often used interchangeably, it's important to understand that electricity is just one component of total energy consumption.

Let's take a look at electricity data. This interactive chart shows the amount of electricity generated by a country each year.

Per capita: which countries generate the most electricity ?

Just as with total energy, comparisons of levels of electricity generation often reflect population size. It tells us nothing about how much electricity the average person in a given country consumes relative to another.

This interactive chart shows per capita electricity generation per person. Again we see vast differences in electricity per person across the world. The largest producers — Iceland, Norway, Sweden, and Canada — generate hundreds of times as much electricity as the smallest.

In many of the poorest countries in the world, people consume very little electricity, which is estimated to be lower than 100 kilowatt-hours per person in some places.

Energy production and consumption by source

This page focuses on total energy and electricity consumption, without digging into the details of where this energy comes from, and how sources are changing over time.

In our pages on the Energy Mix and Electricity Mix , we look at full breakdowns of the energy system; how much of our energy comes from fossil fuels versus low-carbon sources; and whether we're making progress on decarbonization.

Vaclav Smil (2017). Energy Transitions: Global and National Perspectives .

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A Comprehensive Study of Electricity Consumption of People in Each Region in the Philippines

Renz Chester Rosales Gumaru

Math Department, Arellano University, Pasay, Philippines

Renren Banta

Math Department, Alabang High School, Alabang, Philippines

Add to Mendeley

Electricity is one of the most important source of energy in our everyday lives. We use it every day in different situations and circumstances. However, it is also the most used and most expensive energy that we always use. This study will determine which region in the Philippines consumed the most and least electricity. It will also study the relationship between the different distributor of electricity and electricity consumption. This study will help the society in solving the problem of dealing will high cost of electricity. Knowing which regions are the most and least consuming in terms of electricity will surely help the government identify why that particular regions consume a lot, or consume less. This study will also determine if there are changes every month in the most and least consuming electricity regions. The researchers also explore different and various problems and factors contributing to the consumption of electricity of each region in the Philippines. And the researchers also seek if there is a significant difference in the consumption of electricity between regions in the National Capital Region, and I n province. This study will surely help and contribute a lot to a developing country like the Philippines, especially the country is known for consumption of too much electricity.

Published in	( )
DOI
Page(s)	45-48
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License ( ), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Electricity, Energy, Philippines, Source, Power

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Renz Chester Rosales Gumaru, Renren Banta. (2019). A Comprehensive Study of Electricity Consumption of People in Each Region in the Philippines. American Journal of Mechanics and Applications , 7 (3), 45-48. https://doi.org/10.11648/j.ajma.20190703.12

Renz Chester Rosales Gumaru; Renren Banta. A Comprehensive Study of Electricity Consumption of People in Each Region in the Philippines. Am. J. Mech. Appl. 2019 , 7 (3), 45-48. doi: 10.11648/j.ajma.20190703.12

Renz Chester Rosales Gumaru, Renren Banta. A Comprehensive Study of Electricity Consumption of People in Each Region in the Philippines. Am J Mech Appl . 2019;7(3):45-48. doi: 10.11648/j.ajma.20190703.12

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MS Thesis Defense: Scott Maurer

"identifying savings in residential electricity consumption through data-driven analysis".

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Abstract : Climate change and rising electricity demand have driven the need for energy efficiency in buildings. Single-family residential buildings present significant opportunities for improvement, but current strategies for identifying savings are labor and capital-intensive. Developing a residential version of virtual energy audit tools like EDIFES offers a cheaper, non-intrusive alternative.

This study used data from 25 all-electric homes in Florida to refine algorithms from EDIFES, originally designed for commercial buildings. The analysis utilized one year of 15-minute interval electricity consumption data, along with square footage, number of floors, and location. These markers include heating and cooling type, setpoint setback, HVAC load, pool pump detection, refrigeration load, and effective Rvalue. The heating and cooling type marker uses linear or segmented models based on daily consumption and outdoor temperatures. The type of heating and cooling systems were correctly identified in all but one building. A 1°C shift in setpoints resulted in an average 10% annual reduction in electricity consumption. The setpoint marker also estimated HVAC load with an 11% mean error, with 40 of 50 cases having errors under 20%. Pool pump markers, designed for constant-speed pumps, used kernel density to calculate their size and detected their presence with 100% accuracy. Validation with submeter data showed a 4.2% average error in load prediction. Reducing pump schedules to five hours per day led to a 21% reduction in pump load while replacing pumps with variable-speed models resulted in a 23% reduction in whole-home consumption. The refrigeration load marker identified cyclical loads, mainly from the compressor, with a 15% error. However, expected savings are minimal due to the small size of the refrigeration load. Effective R-value was calculated using consumption and temperature data. If R-values were below ASHRAE 90.2 standards, insulation improvements were simulated to quantify potential savings.

Surprisingly, few buildings had lower-than-desired R-values, indicating a need for further study to refine the assumed parameters. The largest and most cost-effective savings came from pool pump replacements and setpoint setbacks. Replacing pumps requires a modest investment but offers significant savings with a quick payback. Setpoint setbacks involve no capital cost and provide immediate savings.

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