Chapagain, K.; Gurung, S.; Kulthanavit, P.; Kittipiyakul, S. Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data. Appl. Syst. Innov.2023, 6, 100.
Chapagain, K.; Gurung, S.; Kulthanavit, P.; Kittipiyakul, S. Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data. Appl. Syst. Innov. 2023, 6, 100.
Chapagain, K.; Gurung, S.; Kulthanavit, P.; Kittipiyakul, S. Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data. Appl. Syst. Innov.2023, 6, 100.
Chapagain, K.; Gurung, S.; Kulthanavit, P.; Kittipiyakul, S. Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data. Appl. Syst. Innov. 2023, 6, 100.
Abstract
Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor to optimize power generation, consumption, saving energy resources, and determining the energy prices. However, integrating energy mix scenarios, including solar and wind power which are highly non-linear and seasonal, into an existing grid increases uncertainty in generation, adds the challenges for precise forecast. To tackle these challenges, state-of-the-art methods and algorithms have been implemented in literature. We have developed Artificial Intelligence (AI) based deep learning models that can effectively handle the information of long time-series data. Based on the pattern of dataset, four different scenarios were developed and two best scenarios were selected for prediction. Dozens of models were developed and tested in deep AI networks. In the first scenario (Scenario1), data for weekdays excluding holidays was taken and in the second scenario (Scenario2) all the data in the basket was taken. Remaining two scenarios, weekends and holidays were tested and neglected because of their high prediction error. To find the optimal configuration, models were trained and tested within a large space of alternatives called hyper-parameters. In this study, an Aritificial Neural Network (ANN) based Feed-forward Neural Network (FNN) showed the minimum prediction error for Scenario1 while a Recurrent Neural Network (RNN) based Gated Recurrent Network (GRU) showed the minimum prediction error for Scenario2. While comparing the accuracy, the lowest MAPE of 2.47% was obtained from FNN for Scenario1. When evaluating the same testing dataset (non-holidays) of Scenario2, the RNN-GRU model achieved the lowest MAPE of 2.71%. Therefore, we can conclude that grouping of weekdays as Senario1 prepared by excluding the holidays provides better forecasting accuracy compared to the single group approach used in Scenario2, where all the dataset is considered together. However, Scenario2 is equally important to predict the demand for weekends and holidays.
Engineering, Electrical and Electronic Engineering
Copyright:
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