Electric consumption forecasting using advanced models offers crucial insights into energy management, especially in regions like New England where demand varies significantly by season. Recent developments in machine learning and deep learning techniques enable more accurate predictions that can lead to better energy resource allocation, reducing operational costs and enhancing reliability.
Researchers propose a forecasting model specifically designed for short-term electricity demand in the New England Control Area (ISO-NE-CA). By incorporating seasonal variations and employing algorithms such as Adaptive Neural-based Fuzzy Inference Systems (ANFIS), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Artificial Neural Networks (ANN), the study aims to refine the accuracy of electricity demand forecasts. An emphasis is placed on how temperature influences electricity demands across different types of days—be it working, weekends, or special holidays.
The framework utilizes a comprehensive dataset spanning a year, analyzing hourly energy consumption corresponding to temperature changes and cost fluctuations. With machine learning techniques, the research reveals significant accuracy in demand prediction—up to 99.9% during winter months using ANFIS, signifying a robust advancement in forecasting methodologies applicable to power markets.
Notably, the effects of temperature on power demand are markedly different on working days compared to weekends or holidays. Researchers found that demand peaks during weekdays when combined with high temperatures, linked with the extensive use of air conditioning. In contrast, lower demand was noted on holidays, although substantial fluctuations were still present.
The models were evaluated based on various performance metrics, including the Mean Absolute Error (MAE) and Normalized Root Mean Squared Error (NRMSE). This analytical approach confirms that the machine and deep learning algorithms not only meet efficiency standards but also call out the seasonal dynamics affecting energy consumption.
As the global shift toward personalized energy consumption accelerates, such forecasting enhancements can play an instrumental role in energy management strategies. With utilities facing an increasing demand for real-time data and accurate forecasting, these findings set a precedence for future incorporation of artificial intelligence into energy sectors.
Ultimately, the study underscores the necessity of integrating advanced forecasting models into energy planning frameworks, paving the way for seamless transitions to more efficient energy consumption practices across varied sectors.
