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Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

Author

Listed:
  • Krzysztof Gajowniczek

    (Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Tomasz Ząbkowski

    (Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-787 Warsaw, Poland)

Abstract

Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand peaks in the power system, forecasts are inaccurate and prone to high numbers of errors. In this paper, our contributions comprise a proposed data-mining scheme for demand modeling through peak detection, as well as the use of this information to feed the forecasting system. For this purpose, we have taken a different approach from that of time series forecasting, representing it as a two-stage pattern recognition problem. We have developed a peak classification model followed by a forecasting model to estimate an aggregated demand volume. We have utilized a set of machine learning algorithms to benefit from both accurate detection of the peaks and precise forecasts, as applied to the Polish power system. The key finding is that the algorithms can detect 96.3% of electricity peaks (load value equal to or above the 99th percentile of the load distribution) and deliver accurate forecasts, with mean absolute percentage error (MAPE) of 3.10% and resistant mean absolute percentage error (r-MAPE) of 2.70% for the 24 h forecasting horizon.

Suggested Citation

  • Krzysztof Gajowniczek & Tomasz Ząbkowski, 2017. "Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms," Energies, MDPI, vol. 10(10), pages 1-25, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1547-:d:114315
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    as
    1. Fakhri J. Hasanov & Lester C. Hunt & Ceyhun I. Mikayilov, 2016. "Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques," Energies, MDPI, vol. 9(12), pages 1-31, December.
    2. Beenstock, Michael & Goldin, Ephraim & Nabot, Dan, 1999. "The demand for electricity in Israel," Energy Economics, Elsevier, vol. 21(2), pages 168-183, April.
    3. Ang, B.W., 1995. "Decomposition methodology in industrial energy demand analysis," Energy, Elsevier, vol. 20(11), pages 1081-1095.
    4. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    5. Javed, Fahad & Arshad, Naveed & Wallin, Fredrik & Vassileva, Iana & Dahlquist, Erik, 2012. "Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting," Applied Energy, Elsevier, vol. 96(C), pages 150-160.
    6. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    7. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    8. De Martino Jannuzzi, Gilberto & Schipper, Lee, 1991. "The structure of electricity demand in the Brazilian household sector," Energy Policy, Elsevier, vol. 19(9), pages 879-891, November.
    9. Seunghyoung Ryu & Jaekoo Noh & Hongseok Kim, 2016. "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, MDPI, vol. 10(1), pages 1-20, December.
    10. Lee, Chien-Chiang & Chang, Chun-Ping, 2007. "The impact of energy consumption on economic growth: Evidence from linear and nonlinear models in Taiwan," Energy, Elsevier, vol. 32(12), pages 2282-2294.
    11. Samouilidis, J. -E. & Mitropoulos, C. S., 1984. "Energy and economic growth in industrializing countries : The case of Greece," Energy Economics, Elsevier, vol. 6(3), pages 191-201, July.
    12. Goia, Aldo & May, Caterina & Fusai, Gianluca, 2010. "Functional clustering and linear regression for peak load forecasting," International Journal of Forecasting, Elsevier, vol. 26(4), pages 700-711, October.
    13. Pokharel, Shaligram, 2007. "An econometric analysis of energy consumption in Nepal," Energy Policy, Elsevier, vol. 35(1), pages 350-361, January.
    14. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    15. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    16. Kim, Sung Hyun & Kim, Tae Heon & Kim, Youngduk & Na, In-Gang, 2001. "Korean energy demand in the new millenium: outlook and policy implications, 2000-2005," Energy Policy, Elsevier, vol. 29(11), pages 899-910, September.
    17. Ozturk, Harun Kemal & Ceylan, Halim & Canyurt, Olcay Ersel & Hepbasli, Arif, 2005. "Electricity estimation using genetic algorithm approach: a case study of Turkey," Energy, Elsevier, vol. 30(7), pages 1003-1012.
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    7. Zigui Jiang & Rongheng Lin & Fangchun Yang, 2018. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data," Energies, MDPI, vol. 11(9), pages 1-19, August.
    8. Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting," Complexity, Hindawi, vol. 2018, pages 1-21, April.

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