Abstract
Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.
Highlights
Predicting the power demand is very important for the stable operation of power systems with fluctuating power demands
Classification-based models applied to electric power load forecasting are k-nearest neighbors (k-NN) [25] and decision trees (DT) [26]
The present study proposed that power demand forecasting includes special days during the week and accounts for buildings with different power consumption patterns
Summary
Predicting the power demand is very important for the stable operation of power systems with fluctuating power demands. Black box-based approaches are commonly referred to as “data-driven models” These approaches rely on time-series statistical analyses and machine learning to assess and forecast electricity consumption [15,16,17]. Classification-based models applied to electric power load forecasting are k-nearest neighbors (k-NN) [25] and decision trees (DT) [26] Both are intuitive models with high predictive accuracy, they are limited owing to their need for a comprehensive set of input data. Some of the most popular artificial intelligence models are support vector machine (SVM) [28], artificial neural networks (ANN) [29], deep neural network (DNN) [30], and long short-term memory (LSTM) [31] These models-based forecasting algorithms lead to less operator-dependent and more versatile methods in terms of data usage, with much higher forecasting accuracy.
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