Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data

Abstract

Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor in optimizing power generation and consumption, saving energy resources, and determining energy prices. However, integrating energy mix scenarios, including solar and wind power, which are highly nonlinear and seasonal, into an existing grid increases the uncertainty of generation, creating additional challenges for precise forecasting. To tackle such challenges, state-of-the-art methods and algorithms have been implemented in the literature. Artificial Intelligence (AI)-based deep learning models can effectively handle the information of long time-series data. Based on patterns identified in datasets, various scenarios can be developed. In this paper, several models were constructed and tested using deep AI networks in two different scenarios: Scenario1 used data for weekdays, excluding holidays, while Scenario2 used the data without exclusion. To find the optimal configuration, the models were trained and tested within a large space of alternative hyperparameters. We used an Artificial Neural Network (ANN)-based Feedforward Neural Network (FNN) to show the minimum prediction error for Scenario1 and a Recurrent Neural Network (RNN)-based Gated Recurrent Network (GRU) to show the minimum prediction error for Scenario2. From our results, it can be concluded that the weekday dataset in Scenario1 prepared by excluding weekends and holidays provides better forecasting accuracy compared to the holistic dataset approach used in Scenario2. However, Scenario2 is necessary for predicting the demand on weekends and holidays.

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

“…The influence of temperature on peak demand is negligible. This fact confirms the assertion made in article [59] that temperature has a negligible effect on commercial demand. When the temperature falls below 30 • C or rises above 35 • C, there is a significant fluctuation in demand during holidays.…”

Enhancing Energy Efficiency With Ai: A Review of Machine Learning Models in Electricity Demand Forecasting

“…The study found that the Artificial Neural Network (ANN)-based Feedforward Neural Network (FNN) showed the minimum prediction error for the first scenario, while the Recurrent Neural Network (RNN)-based Gated Recurrent Network (GRU) was most effective for the second scenario. This research underscores the adaptability of deep learning models in handling complex, nonlinear, and seasonal data in electricity demand forecasting (Chapagain et al, 2023). Another innovative approach is presented in the study by Kim et al (2023), which developed a flexible renewable energy planning based on multi-step forecasting of interregional electricity supply and demand.…”

“…The research reveals that statistical models, Support Vector Machines, and Neural Networks are particularly effective in forecasting, demonstrating the potential of AI in managing the complexities of renewable energy integration (Tervo, 2023). Chapagain et al (2023) conducted a study on short-term electricity demand forecasting using deep neural networks, analyzing Thai data. The research constructed several models using deep AI networks in two different scenarios: one excluding weekends and holidays and the other without exclusions.…”

Data-Driven Adaptive Controller Based on Hyperbolic Cost Function for Non-Affine Discrete-Time Systems with Variant Control Direction

“…With these sophisticated systems, there has been a surge in the availability of system status information, enabling adaptive controllers to rely less on system knowledge. This shift has led to the emergence of data-driven controllers (DDCs) and model estimators [3,4]. Researchers typically categorize the adaptation of DDCs into online learning, offline learning, and hybrid approaches that combine both online and offline learning strategies.…”