标题： 一种面向长期小时电力需求预测的稳定性驱动框架 显示英文标题

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

摘要： 长期电力需求预测对于电网和运营规划，以及能源转型策略的分析和规划至关重要。 然而，具有高时间分辨率的长期负荷预测仍然具有挑战性，因为大多数现有方法侧重于汇总预测，这需要对大量变量进行准确预测以进行自下而上的分部门预测。 在本研究中，我们提出了一种简洁的方法，该方法使用t检验来验证负荷稳定性，并利用负荷与国内生产总值（GDP）的相关性来生成长期每小时负荷预测。 将此方法应用于新加坡的电力需求，对多年历史数据（2004-2022年）的分析表明，其相对每小时负荷保持统计稳定，各季节性指数的整体百分比偏差为4.24%。 利用这些稳定性结果，使用GDP作为预测因子生成了五年 ahead 的总年度预测，并使用每小时季节性指数比例预测每小时负荷。 在多次实验中，六年 ahead 预测的最大平均绝对百分比误差（MAPE）为6.87%。 该方法进一步应用于比利时（经合组织国家）和保加利亚（非经合组织国家），分别得到了6.81%和5.64%的MAPE值。 此外，稳定性结果被整合到基于指数平滑的短期预测模型中，显示出与现有基于机器学习的方法相当或更优的准确性。 这些发现表明，简洁的方法可以有效地生成长期、高分辨率的预测。 显示英文摘要

摘要： 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.