An Experimental Machine Learning Approach for Mid-Term Energy Demand Forecasting

Abstract

In this study, a neural network-based approach is designed for mid-term load forecasting (MTLF). The structure and hyperparameters are tuned to obtain the best forecasting accuracy one year ahead. The suggested approach is practically applied to a region in Iran by the use of real-world data sets of 10 years. The influential factors such as economic, weather, and social factors are investigated, and their impact on accuracy is numerically analyzed. The bad data are detected by a suggested effective method. In addition to load peak, the 24-hours load pattern is also predicted, which helps for better mid-term planning. The simulations show that the suggested approach is practical, and the accuracy is more than 95%, even when there are drastic weather changes.