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Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control

Author

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  • Mirosław Parol

    (Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Paweł Piotrowski

    (Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Piotr Kapler

    (Faculty of Electrical Engineering, Warsaw University of Technology, Koszykowa 75 Street, 00-662 Warsaw, Poland)

  • Mariusz Piotrowski

    (MP System Mariusz Piotrowski, Przejezdna 6/25 Street, 16-001 Ignatki-Osiedle, Poland)

Abstract

This paper addresses very short-term (10 s) forecasting of power demand of highly variable loads. The main purpose of this study is to develop methods useful for this type of forecast. We have completed a comprehensive study using two different time series, which are very difficult to access in practice, of 10 s power demand characterized by big dynamics of load changes. This is an emerging and promising forecasting research topic, yet to be more widely recognized in the forecasting research community. This problem is particularly important in microgrids, i.e., small energy micro-systems. Power demand forecasting, like forecasting of renewable power generation, is of key importance, especially in island mode operation of microgrids. This is due to the necessity of ensuring reliable power supplies to consumers. Inaccurate very short-term forecasts can cause improper operation of microgrids or increase costs/decrease profits in the electricity market. This paper presents a detailed statistical analysis of data for two sample low voltage loads characterized by large variability, which are located in a sewage treatment plant. The experience of the authors of this paper is that very short-term forecasting is very difficult for such loads. Special attention has been paid to different forecasting methods, which can be applied to this type of forecast, and to the selection of explanatory variables in these methods. Some of the ensemble models (eight selected models belonging to the following classes of methods: random forest regression, gradient boosted trees, weighted averaging ensemble, machine learning) proposed in the scope of choice of methods sets constituting the models set are unique models developed by the authors of this study. The obtained forecasts are presented and analyzed in detail. Moreover, qualitative analysis of the forecasts obtained has been carried out. We analyze various measures of forecasts quality. We think that some of the presented forecasting methods are promising for practical applications, i.e., for microgrid operation control, because of their accuracy and stability. The analysis of usefulness of various forecasting methods for two independent time series is an essential, very valuable element of the study carried out. Thanks to this, reliability of conclusions concerning the preferred methods has considerably increased.

Suggested Citation

  • Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1290-:d:506463
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    References listed on IDEAS

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    1. Namrye Son & Seunghak Yang & Jeongseung Na, 2019. "Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory," Energies, MDPI, vol. 12(20), pages 1-17, October.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    3. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    4. Haoran Zhao & Huiru Zhao & Sen Guo, 2018. "Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-19, March.
    5. Jing Huang & John Boland, 2018. "Performance Analysis for One-Step-Ahead Forecasting of Hybrid Solar and Wind Energy on Short Time Scales," Energies, MDPI, vol. 11(5), pages 1-12, May.
    6. Javier Huertas Tato & Miguel Centeno Brito, 2018. "Using Smart Persistence and Random Forests to Predict Photovoltaic Energy Production," Energies, MDPI, vol. 12(1), pages 1-12, December.
    7. Sizhou Sun & Jingqi Fu & Ang Li, 2019. "A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches," Energies, MDPI, vol. 12(18), pages 1-22, September.
    8. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    9. Dongxiao Niu & Di Pu & Shuyu Dai, 2018. "Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm," Energies, MDPI, vol. 11(5), pages 1-21, April.
    10. Jianguo Zhou & Xuechao Yu & Baoling Jin, 2018. "Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    11. Fang Liu & Ranran Li & Aliona Dreglea, 2019. "Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model," Energies, MDPI, vol. 12(18), pages 1-16, September.
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    2. Paweł Piotrowski & Mirosław Parol & Piotr Kapler & Bartosz Fetliński, 2022. "Advanced Forecasting Methods of 5-Minute Power Generation in a PV System for Microgrid Operation Control," Energies, MDPI, vol. 15(7), pages 1-23, April.
    3. Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    4. Grzegorz Dudek, 2021. "Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights," Energies, MDPI, vol. 14(11), pages 1-18, May.
    5. Dimitris Al. Katsaprakakis & Apostolos Michopoulos & Vasiliki Skoulou & Eirini Dakanali & Aggeliki Maragkaki & Stavroula Pappa & Ioannis Antonakakis & Dimitris Christakis & Constantinos Condaxakis, 2022. "A Multidisciplinary Approach for an Effective and Rational Energy Transition in Crete Island, Greece," Energies, MDPI, vol. 15(9), pages 1-49, April.

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