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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15001 |
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| _version_ | 1866913950159863808 |
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| author | Dhar, Soumyadeep Parmar, Ayushkumar Qiu, Haifeng Senga, Juan Ramon L. Viswanathan, S. |
| author_facet | Dhar, Soumyadeep Parmar, Ayushkumar Qiu, Haifeng Senga, Juan Ramon L. Viswanathan, S. |
| contents | Long-term electricity demand forecasting is essential for grid and operations planning, as well as for the analysis and planning of energy transition strategies. However, accurate long-term load forecasting with high temporal resolution remains challenging, as most existing approaches focus on aggregated forecasts, which require accurate prediction of numerous variables for bottom-up sectoral forecasts. In this study, we propose a parsimonious methodology that employs t-tests to verify load stability and the correlation of load with gross domestic product (GDP) to produce a long-term hourly load forecast. Applying this method to Singapore's electricity demand, analysis of multi-year historical data (2004-2022) reveals that its relative hourly load has remained statistically stable, with an overall percentage deviation of 4.24% across seasonality indices. Utilizing these stability findings, five-year-ahead total yearly forecasts were generated using GDP as a predictor, and hourly loads were forecasted using hourly seasonality index fractions. The maximum Mean Absolute Percentage Error (MAPE) across multiple experiments for six-year-ahead forecasts was 6.87%. The methodology was further applied to Belgium (an OECD country) and Bulgaria (a non-OECD country), yielding MAPE values of 6.81% and 5.64%, respectively. Additionally, stability results were incorporated into a short-term forecasting model based on exponential smoothing, demonstrating comparable or improved accuracy relative to existing machine learning-based methods. These findings indicate that parsimonious approaches can effectively produce long-term, high-resolution forecasts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Stability-Driven Framework for Long-Term Hourly Electricity Demand Forecasting Dhar, Soumyadeep Parmar, Ayushkumar Qiu, Haifeng Senga, Juan Ramon L. Viswanathan, S. Methodology Applications Long-term electricity demand forecasting is essential for grid and operations planning, as well as for the analysis and planning of energy transition strategies. However, accurate long-term load forecasting with high temporal resolution remains challenging, as most existing approaches focus on aggregated forecasts, which require accurate prediction of numerous variables for bottom-up sectoral forecasts. In this study, we propose a parsimonious methodology that employs t-tests to verify load stability and the correlation of load with gross domestic product (GDP) to produce a long-term hourly load forecast. Applying this method to Singapore's electricity demand, analysis of multi-year historical data (2004-2022) reveals that its relative hourly load has remained statistically stable, with an overall percentage deviation of 4.24% across seasonality indices. Utilizing these stability findings, five-year-ahead total yearly forecasts were generated using GDP as a predictor, and hourly loads were forecasted using hourly seasonality index fractions. The maximum Mean Absolute Percentage Error (MAPE) across multiple experiments for six-year-ahead forecasts was 6.87%. The methodology was further applied to Belgium (an OECD country) and Bulgaria (a non-OECD country), yielding MAPE values of 6.81% and 5.64%, respectively. Additionally, stability results were incorporated into a short-term forecasting model based on exponential smoothing, demonstrating comparable or improved accuracy relative to existing machine learning-based methods. These findings indicate that parsimonious approaches can effectively produce long-term, high-resolution forecasts. |
| title | A Stability-Driven Framework for Long-Term Hourly Electricity Demand Forecasting |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2507.15001 |