Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors

The purpose of this paper is to suggest short-term Seasonal forecasting for hourly electricity demand in the New England Control Area (ISO-NE-CA). Precision improvements are also considered when creating a model. Where the whole database is split into four seasons based on demand patterns. This article's integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. The linear relationship between temperature and electricity consumption makes the relationship noteworthy. Comparing the temperature effect in a working day and a temperature effect on a weekend day where at night, the marginal effects of temperature on the demand in a working day for power are likewise at their highest. However, there are significant effects of temperature on the demand for a holiday, even a weekend or special holiday. Two scenarios are used to get the results by using machine and deep learning techniques in four seasons. The first scenario is to forecast a working day, and the second scenario is to forecast a holiday (weekend or special holiday) under the effect of the temperature in each of the four seasons and the cost of electricity. To clarify the four techniques' performance and effectiveness, the results were compared using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE) values. The forecasting model shows that the four highlighted algorithms perform well with minimal inaccuracy. Where the highest and the lowest accuracy for the first scenario are (99.90%) in the winter by simulating an Adaptive Neural-based Fuzzy Inference System and (70.20%) in the autumn by simulating Artificial Neural Network. For the second scenario, the highest and the lowest accuracy are (96.50%) in the autumn by simulating Adaptive Neural-based Fuzzy Inference System and (68.40%) in the spring by simulating Long Short-Term Memory. In addition, the highest and the lowest values of Mean Absolute Error (MAE) for the first scenario are (46.6514, and 24.759 MWh) in the spring, and the summer by simulating Artificial Neural Networks. The highest and the lowest values of Mean Absolute Error (MAE) for the second scenario are (190.880, and 45.945 MWh) in the winter, and the autumn by simulating Long Short-Term Memory, and Adaptive Neural-based Fuzzy Inference System.