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

TL;DR

This paper introduces a simple, stability-based framework for long-term hourly electricity demand forecasting that leverages load stability and GDP correlation, achieving high accuracy across multiple countries.

Contribution

The study presents a novel, parsimonious methodology using statistical tests and stability analysis for long-term hourly load prediction, reducing reliance on complex models.

Findings

Achieved maximum MAPE of 6.87% for six-year-ahead forecasts in Singapore.

Validated the approach with similar accuracy in Belgium and Bulgaria.

Enhanced short-term forecasting accuracy by integrating stability analysis into exponential smoothing.

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.