Electric Load Forecasting using Machine Learning for Peak Demand Management in Smart Grids
Received: 22 February 2025 | Revised: 25 March 2025 and 5 April 2025 | Accepted: 7 April 2025 | Online: 27 April 2025
Corresponding author: Mushira Freihat
Abstract
This paper presents an innovative hybrid Deep Learning (DL) approach to address the challenges of electric load forecasting in smart grids, particularly focusing on scenarios involving missing or noisy input data. The study utilizes real-world hourly load data from Qassim city, Saudi Arabia, to develop and validate the proposed model. The hybrid approach combines a Convolutional Neural Network (CNN) with various sequence-learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (BiLSTM), to capture both spatial and temporal dependencies in the dataset. A multi-step preprocessing pipeline ensures data normalization and outlier handling, whereas advanced DL architectures extract and analyze features to improve prediction accuracy. The experimental results demonstrate the hybrid CNN-GRU model's superior performance, achieving lower error rates (e.g., RMSE, NRMSE, and MAPE) compared to traditional methods and standalone DL algorithms. This work underscores the potential of hybrid DL models for enhancing load forecasting accuracy, providing critical insights for optimizing smart grid operations and ensuring sustainable energy management.
Keywords:
energy consumption, machine-learning, electrical consumption rationalizationDownloads
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