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Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market

Author

Listed:
  • Stefan Ungureanu

    (Department of Electric Power Systems and Management, Technical University of Cluj Napoca, 26-28 Baritiu, 400114 Cluj Napoca, Romania)

  • Vasile Topa

    (Department of Electrotechnics and Measurements, Technical University of Cluj Napoca, 26-28 Baritiu, 400114 Cluj Napoca, Romania)

  • Andrei Cristinel Cziker

    (Department of Electric Power Systems and Management, Technical University of Cluj Napoca, 26-28 Baritiu, 400114 Cluj Napoca, Romania)

Abstract

Short-term load forecasting predetermines how power systems operate because electricity production needs to sustain demand at all times and costs. Most load forecasts for the non-residential consumers are empirically done either by a customer’s employee or supplier personnel based on experience and historical data, which is frequently not consistent. Our objective is to develop viable and market-oriented machine learning models for short-term forecasting for non-residential consumers. Multiple algorithms were implemented and compared to identify the best model for a cluster of industrial and commercial consumers. The article concludes that the sliding window approach for supervised learning with recurrent neural networks can learn short and long-term dependencies in time series. The best method implemented for the 24 h forecast is a Gated Recurrent Unit (GRU) applied for aggregated loads over three months of testing data resulted in 5.28% MAPE and minimized the cost with 5326.17 € compared with the second-best method LSTM. We propose a new model to evaluate the gap between evaluation metrics and the financial impact of forecast errors in the power market environment. The model simulates bidding on the power market based on the 24 h forecast and using the Romanian day-ahead market and balancing prices through the testing dataset.

Suggested Citation

  • Stefan Ungureanu & Vasile Topa & Andrei Cristinel Cziker, 2021. "Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market," Energies, MDPI, vol. 14(21), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6966-:d:663032
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    References listed on IDEAS

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    Cited by:

    1. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Mohamad Abou Houran & Varun Kumar & Dinesh Kumar & Saurabh Mani Tripathi & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan, 2023. "A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection," Energies, MDPI, vol. 16(2), pages 1-23, January.
    2. Nikita Dmitrievich Senchilo & Denis Anatolievich Ustinov, 2021. "Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption," Energies, MDPI, vol. 14(21), pages 1-25, October.

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