Optimizing Energy Management Systems with Fine-Grained Interval Demand Forecasting

Accurate electricity demand forecasting is a crucial component of effective energy management. Recent advancements in data analytics and machine learning have enabled the development of advanced forecasting models that leverage historical demand and climate data. Traditional models often relied solely on hourly data and were evaluated at specific time points. However, evaluating the accuracy of predictions across various timeframes, rather than just fixed intervals, can help determine the optimal timing for predictions and the appropriate data window for each forecasting model. Our study demonstrates that utilizing fine-grained interval data, particularly at 30-minute intervals, significantly enhances the precision of these models. By incorporating detailed historical electricity demand data and meteorological information, these models can better predict short-term fluctuations, providing more reliable forecasts. This improvement facilitates more responsive and adaptive energy management strategies, ultimately enhancing overall energy efficiency.