Готові списки джерел за темами / Orecasting of electricity consumption

Добірка наукової літератури з теми "Orecasting of electricity consumption"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Orecasting of electricity consumption"

Stoy, Christian, and Susanne Kytzia. "Benchmarking electricity consumption." Construction Management and Economics 24, no. 10 (October 2006): 1083–89. http://dx.doi.org/10.1080/01446190600799414.

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Qiu, Yueming, Bo Xing, and Yi David Wang. "PREPAID ELECTRICITY PLAN AND ELECTRICITY CONSUMPTION BEHAVIOR." Contemporary Economic Policy 35, no. 1 (February 22, 2016): 125–42. http://dx.doi.org/10.1111/coep.12170.

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M, Sathish Kumar, Sasidharan K, and Sridar G.Srinivas. "Electricity Consumption Notification Application." International Innovative Research Journal of Engineering and Technology 2, no. 1 (September 30, 2016): 1–2. http://dx.doi.org/10.32595/iirjet.org/v2i1.2016.26.

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Akasiadis, Charilaos, and Georgios Chalkiadakis. "Cooperative electricity consumption shifting." Sustainable Energy, Grids and Networks 9 (March 2017): 38–58. http://dx.doi.org/10.1016/j.segan.2016.12.002.

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Andersen, Frits M., Morten S. Christensen, Ole Michael Jensen, Niels-Ulrik Kofoed, and Poul Erik Morthorst. "Second-home electricity consumption." Energy Policy 36, no. 1 (January 2008): 280–89. http://dx.doi.org/10.1016/j.enpol.2007.09.013.

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Ringwood, J. V., P. C. Austin, and W. Monteith. "Forecasting weekly electricity consumption." Energy Economics 15, no. 4 (October 1993): 285–96. http://dx.doi.org/10.1016/0140-9883(93)90018-m.

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Chirwa, Themba G., and Nicholas M. Odhiambo. "Electricity consumption and economic growth." International Journal of Energy Sector Management 14, no. 1 (January 6, 2020): 1–19. http://dx.doi.org/10.1108/ijesm-11-2018-0014.

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Purpose This paper aims to examine the short- and long-run relationship between electricity consumption and economic growth. Design/methodology/approach The study uses a panel-based autoregressive distributed lag approach to cointegration to investigate this relationship in 12 advanced, emerging markets and developing economies during the period 1970-2016, selected from three continents, namely, Europe (Luxemborg, Norway, Denmark and Belgium), Asia (Singapore, Japan, Indonesia and India) and Africa (South Africa, Algeria, Egypt and Kenya). Findings Based on the homogeneity assumption, the study results reveal that electricity consumption is positively and significantly associated with economic growth in all the study countries in the long run. Conversely, the short-run results reveal that electricity consumption is positively and significantly associated with economic growth in ten countries and negatively associated with economic growth in only two countries. Research limitations/implications The study concludes that, on the whole, electricity consumption is an important factor of production in the majority of the study countries. Therefore, policymakers should focus on growth-enhancing energy policies that promote energy efficiency usage, especially in the long run. Originality/value The authors hereby confirm that the paper has not been published elsewhere, and this research is entirely their work.

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CRAWSHAW, A. J. E., D. I. WILLIAMS, and C. M. CRAWSHAW. "Consumer knowledge and electricity consumption." Journal of Consumer Studies and Home Economics 9, no. 4 (December 1985): 283–89. http://dx.doi.org/10.1111/j.1470-6431.1985.tb00099.x.

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Pradhan, Gyanendra Lal. "Electricity: Domestic Consumption Versus Export." Hydro Nepal: Journal of Water, Energy and Environment 3 (May 26, 2009): 16–18. http://dx.doi.org/10.3126/hn.v3i0.1913.

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Due to its unique geography, Nepal is gifted with very high hydropower potential, far greater than generally accepted figure of 83,000 MW and 43000 MW of theoretical and techno-financial viability. Our neighbors India, Pakistan and Bangladesh are suffering from huge power shortages. There is no point in debating whether Nepal’s hydropower should be for domestic consumption or for export because the potential of generating hydropower in Nepal is far greater than its domestic consumption, even in 2050. Due to the lack of appropriate policies, however, Nepal suffers from long hours of load shedding. The government policy to subsidize petroleum products was a big mistake for, like Bhutan, electricity should have been the cheapest source of energy. Politicians are focusing mostly in export oriented projects; whereas, higher importance should have been given to projects for domestic consumption. We should aim at producing twice the needed internal demand. Nepal is poised to reap huge benefits from hydropower.Key words: Hydropower; domestic consumption; load shedding; export; Nepaldoi: 10.3126/hn.v3i0.1913Hydro Nepal Journal of Water, Energy and EnvironmentIssue No. 3, July 2008. Page: 16-18

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Fullerton, Thomas M., David A. Juarez, and Adam G. Walke. "Residential electricity consumption in Seattle." Energy Economics 34, no. 5 (September 2012): 1693–99. http://dx.doi.org/10.1016/j.eneco.2012.02.004.

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Дисертації з теми "Orecasting of electricity consumption"

Крамаренко, Семен Віталійович. "Прогнозування електроспоживання на базі вейвлетів". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/28917.

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Сучасні тенденції на ринку електроніки вимагають пошуку комплексних рішень по прогнозуванні електроспоживання локальних об’єктів. Тому, є актуальним дослідження методів, які при мінімальних розрахунках будуть проводити прогнозування з максимальною точністю. У дипломній роботі були досліджені існуючи методи прогнозування електроспоживання локального об’єкту. Було з’ясовано, що на сьогоднішній день існує два основні методи прогнозування електроспоживання, які передбачають ситуацію з істотними стрибками електроспоживання, а саме: метод прогнозування електроспоживання за допомогою нейронних мереж і метод прогнозування електроспоживання за допомогою вейвлет- перетворень. Було обрано метод прогнозування за допомогою вейвлетів, оскільки він має ряд переваг перед методом прогнозування за допомогою нейронних мереж.
Modern trends in the electronics market require the search for comprehensive solutions for forecasting the local consumption of electricity. Therefore, it is actual research of methods, which at the minimum calculations will carry out forecasting with maximum accuracy. In the work, existing methods of forecasting the local consumption of electricity were investigated. It was found that to date there are two main methods of forecasting electricity consumption, namely: the method of forecasting electricity consumption with the help of neural networks and the method of predicting power consumption using wavelet transformations. Wavelet forecasting method was chosen because it has several advantages over the prognostication method using neural networks.

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GHORBANI, SONIYA. "Anomaly Detection in Electricity Consumption Data." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35011.

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Distribution grids play an important role in delivering electricityto end users. Electricity customers would like to have a continuouselectricity supply without any disturbance. For customerssuch as airports and hospitals electricity interruption may havedevastating consequences. Therefore, many electricity distributioncompanies are looking for ways to prevent power outages.Sometimes the power outages are caused from the grid sidesuch as failure in transformers or a break down in power cablesbecause of wind. And sometimes the outages are caused bythe customers such as overload. In fact, a very high peak inelectricity consumption and irregular load profile may causethese kinds of failures.In this thesis, we used an approach consisting of two mainsteps for detecting customers with irregular load profile. In thefirst step, we create a dictionary based on all common load profileshapes using daily electricity consumption for one-monthperiod. In the second step, the load profile shapes of customersfor a specific week are compared with the load patterns in thedictionary. If the electricity consumption for any customer duringthat week is not similar to any of the load patterns in thedictionary, it will be grouped as an anomaly. In this case, loadprofile data are transformed to symbols using Symbolic AggregateapproXimation (SAX) and then clustered using hierarchicalclustering.The approach is used to detect anomaly in weekly load profileof a data set provided by HEM Nät, a power distributioncompany located in the south of Sweden.

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Vadda, Praveen, and Sreerama Murthy Seelam. "Smart Metering for Smart Electricity Consumption." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2476.

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In recent years, the demand for electricity has increased in households with the use of different appliances. This raises a concern to many developed and developing nations with the demand in immediate increase of electricity. There is a need for consumers or people to track their daily power usage in houses. In Sweden, scarcity of energy resources is faced during the day. So, the responsibility of human to save and control these resources is also important. This research work focuses on a Smart Metering data for distributing the electricity smartly and efficiently to the consumers. The main drawback of previously used traditional meters is that they do not provide information to the consumers, which is accomplished with the help of Smart Meter. A Smart Meter helps consumer to know the information of consumption of electricity for appliances in their respective houses. The aim of this research work is to measure and analyze power consumption using Smart Meter data by conducting case study on various households. In addition of saving electricity, Smart Meter data illustrates the behaviour of consumers in using devices. As power consumption is increasing day by day there should be more focus on understanding consumption patterns i.e. measurement and analysis of consumption over time is required. In case of developing nations, the technology of employing smart electricity meters is still unaware to many common people and electricity utilities. So, there is a large necessity for saving energy by installing these meters. Lowering the energy expenditure by understanding the behavior of consumers and its correlation with electricity spot prices motivated to perform this research. The methodology followed to analyze the outcome of this study is exhibited with the help of a case analysis, ARIMA model using XLSTAT tool and a flattening technique. Based on price evaluation results provided in the research, hypothesis is attained to change the behavior of consumers when they have better control on their habits. This research contributes in measuring the Smart Meter power consumption data in various households and interpretation of the data for hourly measurement could cause consumers to switch consumption to off-peak periods. With the results provided in this research, users can change their behavior when they have better control on their habits. As a result, power consumption patterns of Smart electricity distribution are studied and analyzed, thereby leading to an innovative idea for saving the limited resource of electrical energy.
+91 9908265578

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Mangisa, Siphumlile. "Statistical analysis of electricity demand profiles." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1011548.

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An electricity demand profile is a graph showing the amount of electricity used by customers over a unit of time. It shows the variation in electricity demand versus time. In the demand profiles, the shape of the graph is of utmost importance. The variations in demand profiles are caused by many factors, such as economic and en- vironmental factors. These variations may also be due to changes in the electricity use behaviours of electricity users. This study seeks to model daily profiles of energy demand in South Africa with a model which is a composition of two de Moivre type models. The model has seven parameters, each with a natural interpretation (one parameter representing minimum demand in a day, two parameters representing the time of morning and afternoon peaks, two parameters representing the shape of each peak, and two parameters representing the total energy per peak). With the help of this model, we trace change in the demand profile over a number of years. The proposed model will be helpful for short to long term electricity demand forecasting.

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Gupta, Pavan, University of Western Sydney, College of Law and Business, and School of Management. "Residential sector deregulation in the electricity industry : analysis of electricity consumption patterns." THESIS_CLAB_MAN_Gupta_P.xml, 2004. http://handle.uws.edu.au:8081/1959.7/744.

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The research presented in this thesis aims to improve our knowledge regarding the impact of privatisation and deregulation of public service type infrastructure industries. In recent years, Australia's industry reform policies have critically relied on rapid deregulation of major utilities such as telecommunication, gas and electricity. Although several industries have been deregulated in the last two decades, our understanding regarding the impact of deregulation on residential electricity market is still developing. In order to accomplish the research, about 400 residential customers were surveyed and their electricity consumption patterns (ECP)were monitored by installing special electronic meters. The findings are discussed in detail. As an implication to policy and practice there is an urgent need for a nation-wide standard,reshaping the practices of the electricity marketing and establishing a time-dynamic ECP monitoring system. Another important implication concerns the well-founded theories in micro-economic literature. This research has established that the price of the commodities and services charged by public service type utility suppliers should not be left entirely to the market forces concerned with demand - supply equilibrium. There is an urgent need to understand the role of different socio-economic segments in contributing to the economic efficiencies of public service type assets. More efficient segments should be equitably rewarded for their contributions rather than penalised perhaps due to the lack of their bargaining power.
Doctor of Philosophy (PhD)

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Gupta, Pavan. "Residential sector deregulation in the electricity industry : analysis of electricity consumption patterns /." View thesis, 2004. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20050225.104416/index.html.

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Thesis (Ph.D.) -- University of Western Sydney, 2004.
"A thesis presented to the University of Western Sydney, in partial fulfilment of the requirements of the degree of Doctor of Philosophy" Bibliography : leaves 273-285.

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Daryanian, Bahman. "Scheduling of electricity consumption under spot prices." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14431.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1989.
Title as it appears in M.I.T. Graduate List, Feb. 1989: Optimal scheduling of electricity usage under spot prices.
Includes bibliographical references.
by Bahman Daryanian.
Ph.D.

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Martin, William M. "PAY-AS-YOU-GO ELECTRICITY: THE IMPACT OF PREPAY PROGRAMS ON ELECTRICITY CONSUMPTION." UKnowledge, 2014. http://uknowledge.uky.edu/agecon_etds/29.

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Prepay or pay-as-you-go programs are an increasingly popular type of rate plan offered by electric utilities. Under these plans, ratepayers must keep a positive balance at all times to avoid being automatically disconnected, they are charged daily for their usage, and they are provided with a means to monitor their consumption. One of the suggested benefits of these plans is that they allow electricity consumers to better manage their usage. Using household level monthly usage data from customers enrolled in prepay programs at two Kentucky rural electric cooperatives, we investigate whether there is a change in consumption after these customers enrolled in the program. To address this question, we employ a fixed-effects model. The results of our model indicate that prepay customers reduce their consumption by an average of 11% after enrolling in the program. We also find that this response is larger during periods of high or low temperatures than during mild weather. Furthermore, we find evidence that the prepayment effect diminishes over the length of time that a customer is enrolled in the program.

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Wallace, Eva-Lena. "How the Price of Electricity has Affected the Electricity Demand in the EU-27 During 1998-2008. : - Would an Environmental Tax on Electricity Reduce the Electricity Consumption and Increase the Share of Electricity Generated from Renewable Energy Sources?" Thesis, Umeå universitet, Handelshögskolan vid Umeå universitet (USBE), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-51328.

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Kothapalli, Anil Kumar. "Short-Term electricity consumption prediction: Elområde 4, Sweden." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-87261.

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This Thesis work is part of course work for the Masters Program in Data Science at LTU. The focus of this work is mainly to review the literature published to identify state-of-art methodologies applied to predict short-term electricity consumption. This includes the exploration of features and models as well-as the discussion of the results attained. Identify opportunities to improve the forecast results for southern Elområde(bidding area)4, Sweden. The application of different modern methods to forecast electricity consumption has been studied and experimented with. This work adapted the CRISP-DM, a Data Science methodology.

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Книги з теми "Orecasting of electricity consumption"

Group, Primary Research. College electricity consumption benchmarks. New York?]: Primary Research Group, 2011.

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Scott, S. Domestic electricity demand: Executive summary. Dublin: Economic and Social Research Institute, 1991.

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(India), Karnataka. The Karnataka Electricity (Taxation on Consumption) Act, 1959 and the Karnataka Electricity (Taxation on Consumption) Rules, 1959. Bangalore: Govt. of Karnataka, Dept. of Parliamentary Affairs and Legislation, 2000.

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Exploring the value of electricity. Fairmont Press, Inc: Lilburn, GA, 2015.

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Francisco, Clodualdo R. Demand for electricity in the Philippines: Implications for alternative electricity pricing policies. Legaspi Village, Makati, Metro Manila: Philippine Institute for Development Studies, 1988.

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Reiss, Peter C. Household electricity demand, revisited. Cambridge, MA: National Bureau of Economic Research, 2001.

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Spitler, Jeffrey D. Fan electricity consumption for variable-air-volume systems. Champaign, Ill: US Army Corps of Engineers, Construction Engineering Research Laboratory, 1986.

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United States. General Accounting Office. RCED. Energy consumption: Federal agencies' electricity use and cost. Washington, D.C: The Office, 1997.

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Costa, Dora L. Electricity consumption and durable housing: Understanding Cohort effects. Cambridge, MA: National Bureau of Economic Research, 2011.

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Generating and using electricity. Oxford: Heinemann Library, 2010.

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Частини книг з теми "Orecasting of electricity consumption"

Bhatia, Gresha, Gurpreet Singh Nagpal, Samujjwaal Dey, Ashish Joshi, and Nadiminti Sai Sirisha. "Interactive Electricity Consumption System." In Smart Systems and IoT: Innovations in Computing, 363–78. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8406-6_35.

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Chen, Qixin, Hongye Guo, Kedi Zheng, and Yi Wang. "Monthly Electricity Consumption Forecasting." In Data Analytics in Power Markets, 43–69. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4975-2_4.

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Dey, Devanjan, Suchit Yeragi, Ruman Maknojia, and Laxmi Patel. "Know Your Electricity Consumption." In Lecture Notes in Electrical Engineering, 279–85. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8727-3_30.

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Soliman, Engy, Al-Hanouf Al-Mohannadi, and Noora Fetais. "Visualizing Electricity Consumption in Qatar." In Lecture Notes in Computer Science, 378–81. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46771-9_49.

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Telatar, M. Erdinc. "Electricity Consumption, GDP and Renewables." In Energy Technology and Valuation Issues, 109–25. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13746-9_6.

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Chen, Yi, and Yun Li. "Quantitative Modeling of Electricity Consumption." In Computational Intelligence Assisted Design, 387–402. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2018. | "A science publishers book.": CRC Press, 2018. http://dx.doi.org/10.1201/9781315153179-25.

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Acar, Buğsem, Selin Yiğit, Berkay Tuzuner, Burcu Özgirgin, İpek Ekiz, Melisa Özbiltekin-Pala, and Esra Ekinci. "Electricity Consumption Forecasting in Turkey." In Lecture Notes in Mechanical Engineering, 702–14. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90421-0_60.

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Hu, Zhaoguang, and Zheng Hu. "The Supply and Demand Models Based on Electricity Consumption." In Electricity Economics: Production Functions with Electricity, 79–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40757-4_4.

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Hu, Zhaoguang, and Zheng Hu. "Features of Electricity Consumption During the Economic Development Phase." In Electricity Economics: Production Functions with Electricity, 227–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40757-4_9.

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Liu, Hui. "Prediction Model of City Electricity Consumption." In Smart Cities: Big Data Prediction Methods and Applications, 51–88. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2837-8_3.

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Тези доповідей конференцій з теми "Orecasting of electricity consumption"

Klemenjak, Christoph, Andreas Reinhardt, Lucas Pereira, Stephen Makonin, Mario Bergés, and Wilfried Elmenreich. "Electricity Consumption Data Sets." In BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3360322.3360867.

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Bimenyimana, Samuel, Godwin Norense Osarumwense Asemota, Paula Jeanne Ihirwe, and Lingling Li. "Clustering Residential Electricity Consumption." In EEET '18: 2018 International Conference on Electronics and Electrical Engineering Technology. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3277453.3277465.

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Afonin, V. I., N. P. Badaljan, G. V. Maslakova, A. A. Mitrofanov, and E. A. Chashchin. "Electricity Consumption In Lifts." In 2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2021. http://dx.doi.org/10.1109/summa53307.2021.9632037.

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Oboskalov, V., and T. Panikovskaya. "Electricity consumption management in a competitive electricity market." In ENERGY QUEST 2014. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/eq140111.

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Gostautas, Ignas. "Electricity consumption and house values." In 22nd Annual European Real Estate Society Conference. European Real Estate Society, 2015. http://dx.doi.org/10.15396/eres2015_256.

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Pang, Yue, Chaoyi Jin, Xiangdong Zhou, Naiwang Guo, and Yong Zhang. "Hierarchical Electricity Demand Forecasting by Exploring the Electricity Consumption Patterns." In 7th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006715005760581.

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Doronin, Anton. "CLOUD SERVICE FOR ELECTRICITY CONSUMPTION FORECASTING." In 19th SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings. STEF92 Technology, 2019. http://dx.doi.org/10.5593/sgem2019/2.1/s07.020.

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Ardakanian, Omid, Srinivasan Keshav, and Catherine Rosenberg. "Markovian models for home electricity consumption." In the 2nd ACM SIGCOMM workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2018536.2018544.

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Binh, P. T. T., and L. D. Tuong. "Clustering the behaviour of electricity consumption." In 2012 10th International Power & Energy Conference (IPEC). IEEE, 2012. http://dx.doi.org/10.1109/asscc.2012.6523301.

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Yamnenko, Julia, Tetyana Tereshchenko, Liubov Klepach, and Dmitry Palii. "Forecasting of electricity consumption in SmartGrid." In 2017 International Conference on Modern Electrical and Energy Systems (MEES). IEEE, 2017. http://dx.doi.org/10.1109/mees.2017.8248891.

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Звіти організацій з теми "Orecasting of electricity consumption"

Costa, Dora, and Matthew Kahn. Electricity Consumption and Durable Housing: Understanding Cohort Effects. Cambridge, MA: National Bureau of Economic Research, January 2011. http://dx.doi.org/10.3386/w16732.

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Spitler, Jeffrey D., and Douglas C. Hittle. Fan Electricity Consumption for Variable-Air-Volume Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada173399.

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Casas-Lozano, Maria Camila. Subsidies to electricity consumption and housing demand in Bogotá. Bogotá, Colombia: Banco de la República, October 2014. http://dx.doi.org/10.32468/be.847.

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Farhar, B. C., and C. Fitzpatrick. Effects of feedback on residential electricity consumption: A literature review. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/6238465.

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Meyers, S., and C. Campbell. Regional electricity supply and consumption in developing countries, 1980--1986. Office of Scientific and Technical Information (OSTI), March 1989. http://dx.doi.org/10.2172/5380110.

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Macknick, Jordan, Robin Newmark, Garvin Heath, and K. C. Hallett. Review of Operational Water Consumption and Withdrawal Factors for Electricity Generating Technologies. Office of Scientific and Technical Information (OSTI), March 2011. http://dx.doi.org/10.2172/1009674.

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Macknick, Jordan, Robin Newmark, Garvin Heath, and K. C. Hallett. A Review of Operational Water Consumption and Withdrawal Factors for Electricity Generating Technologies. Office of Scientific and Technical Information (OSTI), March 2011. http://dx.doi.org/10.2172/1219149.

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Burkhardt, Jesse, Kenneth Gillingham, and Praveen Kopalle. Experimental Evidence on the Effect of Information and Pricing on Residential Electricity Consumption. Cambridge, MA: National Bureau of Economic Research, February 2019. http://dx.doi.org/10.3386/w25576.

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Hasanov, Fakhri. Theoretical Framework for Industrial Electricity Consumption Revisited: Empirical Analysis and Projections for Saudi Arabia. King Abdullah Petroleum Studies and Research Center, November 2019. http://dx.doi.org/10.30573/ks--2019-dp66.

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Kahn, Matthew, Nils Kok, and John Quigley. Commercial Building Electricity Consumption Dynamics: The Role of Structure Quality, Human Capital, and Contract Incentives. Cambridge, MA: National Bureau of Economic Research, February 2013. http://dx.doi.org/10.3386/w18781.

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