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Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks

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  • Sulandari, Winita
  • Subanar,
  • Lee, Muhammad Hisyam
  • Rodrigues, Paulo Canas

Abstract

Electricity plays a key role in human life. This study presents several methods to forecast Indonesian electricity load demand and compares the performance of the methods. The Indonesian hourly and half-hourly load series tend to have multiple seasonal patterns. Singular Spectrum Analysis (SSA) is chosen because of its capability in decomposing the series into two separable components, a combination of cyclist and seasonal series and noise (irregular) components. In this paper we propose to model time series data by obtaining the forecast values with SSA considering the Linear Recurrent Formula (LRF) and, afterwards, to model the irregular component by fuzzy systems and neural networks (NN). The forecast values obtained from SSA-LRF are then compared with the forecast values obtained from the combining methods, i.e. SSA-LRF-Fuzzy and SSA-LRF-NN. Based on RMSE and MAPE, the SSA-LRF-NN is the most appropriate method to predict the future values of electricity load series. Four Indonesian electricity load data sets were considered in this study to validate the effectiveness of the proposed hybrid methods. The results show that the proposed methods, namely the SSA-LRF-NN algorithm can reduce the RMSE for the testing data from that obtained by SSA-LRF up to 83%.

Suggested Citation

  • Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s0360544219321036
    DOI: 10.1016/j.energy.2019.116408
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    3. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
    4. Hasnain Iftikhar & Nadeela Bibi & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan," Energies, MDPI, vol. 16(6), pages 1-17, March.
    5. Jeseok Ryu & Jinho Kim, 2020. "Demand Response Program Expansion in Korea through Particulate Matter Forecasting Based on Deep Learning and Fuzzy Inference," Energies, MDPI, vol. 13(23), pages 1-14, December.
    6. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
    7. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.
    8. Winita Sulandari & Yudho Yudhanto & Paulo Canas Rodrigues, 2022. "The Use of Singular Spectrum Analysis and K-Means Clustering-Based Bootstrap to Improve Multistep Ahead Load Forecasting," Energies, MDPI, vol. 15(16), pages 1-22, August.
    9. Aneeque A. Mir & Mohammed Alghassab & Kafait Ullah & Zafar A. Khan & Yuehong Lu & Muhammad Imran, 2020. "A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons," Sustainability, MDPI, vol. 12(15), pages 1-35, July.
    10. Khondaker Golam Moazzem & Helen Mashiyat Preoty, 2021. "Proposed Power and Energy System Master Plan (PESMP): Perspective on Analytical Frame, Methodology and Influencing Factors on Demand Forecasting," CPD Working Paper 139, Centre for Policy Dialogue (CPD).
    11. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2021. "Point and interval forecasting of electricity supply via pruned ensembles," Energy, Elsevier, vol. 232(C).
    12. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method," Energies, MDPI, vol. 16(18), pages 1-22, September.
    13. Shi, Jiaqi & Li, Chenxi & Yan, Xiaohe, 2023. "Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization," Energy, Elsevier, vol. 262(PB).
    14. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).

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