Enhancing Short-Term Load Forecasting Using Hyperparameter-Optimized Deep Learning Approaches

Nazmun Nahar Karima

doi:10.3390/en19030705

2026-01-29

Lilik Jamilatul Awalin *

Syahirah Abd Halim

Hazlie Mokhlis Mokhlis

Mohd Syukri Ali

Syahril Mubarok

DOI：10.3390/en19030705

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摘要

En 中文

The reliability and efficiency of power system operations, especially in smart grid scenarios, depend on accurate load demand forecasting. Electrical load forecasting is crucial for power system design, fault protection and diversification as it reduces operating costs while enhancing the system’s overall reliability, stability, and efficiency from an economic and technical perspective. Previously, load forecasting analysis has frequently been limited by inadequate feature engineering and insufficient model tuning. Prediction reliability was reduced by many previous methods’ inabilities to accurately evaluate short-term variations over time and the impact of important variables. These constraints encouraged us to develop a more reliable and thorough forecasting procedure. This research proposes an enhanced short-term load forecasting framework based on a hyperparameter-tuned long short-term memory (LSTM) using a deep learning method recurrent neural network (RNN), alongside more neural network-based models such as artificial neural networks, k-nearest neighbors, and backpropagation neural networks. Hyperparameter optimization techniques (Keras Tuner, Grid SearchCV, Scikeras + Randomized SearchCV, etc.) were used to systematically tune training parameters, learning rates, and network architectures for each forecasting model to increase model accuracy. To provide a more reliable and accurate evaluation of forecasting performance, this research employs the use of an hourly load dataset (2003–2014) enhanced with historical and environmental variables. Significant statistical metrics, such as a mean absolute error of 0.0048, root mean squared error of 0.0091, coefficient of determination of R2 0.9958, and mean absolute percentage error of 1.60%, demonstrate that the hyperparameter optimized with hourly data performed better than both conventional and other deep learning models, with the highest efficiency of all tested models. In accordance with the results, accurate LSTM-RNN parameter modification significantly improves prediction accuracy.

Keyword:

electrical load management

deep learning

short-term electrical load forecasting

hyperparameter

neural network

smart grid

energy efficiency