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Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model

Author

Listed:
  • La Tona, G.
  • Luna, M.
  • Di Piazza, M.C.

Abstract

Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%.

Suggested Citation

  • La Tona, G. & Luna, M. & Di Piazza, M.C., 2024. "Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 63-75.
    • Handle: RePEc:eee:matcom:v:224:y:2024:i:pb:p:63-75
    DOI: 10.1016/j.matcom.2023.06.017
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    References listed on IDEAS

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    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    5. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
    6. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    7. Giuseppe La Tona & Maria Carmela Di Piazza & Massimiliano Luna, 2021. "Effect of Daily Forecasting Frequency on Rolling-Horizon-Based EMS Reducing Electrical Demand Uncertainty in Microgrids," Energies, MDPI, vol. 14(6), pages 1-16, March.
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    1. Lekhel, Cheikh Elekbir Sidi & Mbayed, Rita & Velihorskyi, Oleksandr & Husev, Oleksandr & Monmasson, Eric, 2025. "Generic residential load profile generator based on weather data and occupancy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 237(C), pages 373-389.

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