A Stability-Driven Framework for Long-Term Hourly Electricity Demand Forecasting

2025-07-20

AI Summary

To address the challenge of long-term, high-temporal-resolution electricity demand forecasting, this paper proposes a parsimonious, load-stability-driven forecasting paradigm. First, inter-regional load stability is statistically validated via t-tests and integrated with GDP data for aggregate demand projection. Second, hourly seasonal indices—derived through seasonal decomposition—are employed to allocate total demand across hours, followed by exponential smoothing for fine-grained hourly forecasting. This approach avoids reliance on complex machine learning models, ensuring both interpretability and strong generalization capability. Empirical evaluations across Singapore, Belgium, and Bulgaria demonstrate high accuracy and cross-national robustness: six-year-ahead forecasts achieve mean absolute percentage errors (MAPE) of 6.87%, 6.81%, and 5.64%, respectively. The method thus provides a reliable, transparent, and operationally deployable forecasting tool for power system planning and energy transition initiatives.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Machine Learning: Ensemble Methods

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Accurate long-term hourly electricity demand forecasting
• Stability verification of load patterns using t-tests
• Integration of GDP correlation for load prediction

Innovation

• Uses t-tests for load stability verification
• Correlates load with GDP for forecasting
• Applies hourly seasonality index fractions