Changing Electricity Tariff—An Empirical Analysis Based on Commercial Customers’ Data from Poland
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Granell, Ramon & Axon, Colin J. & Wallom, David C.H., 2014. "Predicting winning and losing businesses when changing electricity tariffs," Applied Energy, Elsevier, vol. 133(C), pages 298-307.
- Thorsnes, Paul & Williams, John & Lawson, Rob, 2012. "Consumer responses to time varying prices for electricity," Energy Policy, Elsevier, vol. 49(C), pages 552-561.
- Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang & Rieger, Alexander & Thimmel, Markus, 2018. "One rate does not fit all: An empirical analysis of electricity tariffs for residential microgrids," Applied Energy, Elsevier, vol. 210(C), pages 800-814.
- Severin Borenstein, 2007. "Wealth Transfers Among Large Customers from Implementing Real-Time Retail Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 131-150.
- Rafik Nafkha & Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques," Energies, MDPI, vol. 11(3), pages 1-17, February.
- Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
- Oprea, Simona-Vasilica & Bâra, Adela & Ciurea, Cristian-Eugen, 2022. "A novel cost-revenue allocation computation for the competitiveness of balancing responsible parties, including RES. Insights from the electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 881-894.
- Ali Hortaçsu & Seyed Ali Madanizadeh & Steven L. Puller, 2017.
"Power to Choose? An Analysis of Consumer Inertia in the Residential Electricity Market,"
American Economic Journal: Economic Policy, American Economic Association, vol. 9(4), pages 192-226, November.
- Ali Hortaçsu & Seyed Ali Madanizadeh & Steven L. Puller, 2015. "Power to Choose? An Analysis of Consumer Inertia in the Residential Electricity Market," NBER Working Papers 20988, National Bureau of Economic Research, Inc.
- Krzysztof Karpio & Piotr Łukasiewicz & Rafik Nafkha, 2023. "New Method of Modeling Daily Energy Consumption," Energies, MDPI, vol. 16(5), pages 1-24, February.
- Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
- Herter, Karen, 2007. "Residential implementation of critical-peak pricing of electricity," Energy Policy, Elsevier, vol. 35(4), pages 2121-2130, April.
- Sharifi, R. & Fathi, S.H. & Vahidinasab, V., 2017. "A review on Demand-side tools in electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 565-572.
- Anna Kowalska-Pyzalska, 2018. "An Empirical Analysis of Green Electricity Adoption Among Residential Consumers in Poland," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Tomasz Ząbkowski & Krzysztof Gajowniczek & Grzegorz Matejko & Jacek Brożyna & Grzegorz Mentel & Małgorzata Charytanowicz & Jolanta Jarnicka & Anna Olwert & Weronika Radziszewska & Jörg Verstraete, 2023. "Cluster-Based Approach to Estimate Demand in the Polish Power System Using Commercial Customers’ Data," Energies, MDPI, vol. 16(24), pages 1-21, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
- Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
- Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
- Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
- Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
- Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
- Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
- Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
- Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
- Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
- Yi Cao & Xue Li, 2022. "Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
- Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
- Andruszkiewicz, Jerzy & Lorenc, Józef & Weychan, Agnieszka, 2020. "Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system," Energy, Elsevier, vol. 196(C).
- Daniel Boller & Michael Lechner & Gabriel Okasa, 2021.
"The Effect of Sport in Online Dating: Evidence from Causal Machine Learning,"
Papers
2104.04601, arXiv.org.
- Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," IZA Discussion Papers 14259, Institute of Labor Economics (IZA).
- Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series 2104, University of St. Gallen, School of Economics and Political Science.
- Jorge Antunes & Peter Wanke & Thiago Fonseca & Yong Tan, 2023. "Do ESG Risk Scores Influence Financial Distress? Evidence from a Dynamic NDEA Approach," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
- Lyudmyla Kirichenko & Tamara Radivilova & Vitalii Bulakh, 2018. "Machine Learning in Classification Time Series with Fractal Properties," Data, MDPI, vol. 4(1), pages 1-13, December.
- Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
- José A. Ferreira, 2022. "Models under which random forests perform badly; consequences for applications," Computational Statistics, Springer, vol. 37(4), pages 1839-1854, September.
- Villacis, Alexis & Badruddoza, Syed & Mayorga, Joaquin & Mishra, Ashok K., 2022. "Using Machine Learning to Test the Consistency of Food Insecurity Measures," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322472, Agricultural and Applied Economics Association.
- Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
More about this item
Keywords
energy consumption; energy efficiency; commercial customers; changing electricity tariff; k-nearest neighbors; classification tree; random forest;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6853-:d:1249365. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.