Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

Meshari D. Alanazi (), Ahmad Saeed, Muhammad Islam, Shabana Habib, Hammad I. Sherazi, Sheroz Khan and Mohammad Munawar Shees
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Meshari D. Alanazi: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Ahmad Saeed: Department of Computer Science and IT, Hazara University Mansehra, Khyber Pakhtunkhwa 21120, Pakistan
Muhammad Islam: Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia
Shabana Habib: Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
Hammad I. Sherazi: Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia
Sheroz Khan: Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia
Mohammad Munawar Shees: Department of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi Arabia

Sustainability, 2023, vol. 15, issue 24, 1-17

Abstract: Accurate short-term forecasting of electrical energy loads is essential for optimizing energy management in low-carbon buildings. This research presents an innovative two-stage model designed to address the unique challenges of Electricity Load Forecasting (ELF). In the first phase, robust data preprocessing techniques are employed to handle issues such as outliers, missing values, and data normalization, which are common in electricity consumption datasets in the context of low-carbon buildings. This data preprocessing enhances data quality and reliability, laying the foundation for accurate modeling. Subsequently, an advanced data-driven modeling approach is introduced. The model combines a novel residual Convolutional Neural Network (CNN) with a layered Echo State Network (ESN) to capture both spatial and temporal dependencies in the data. This innovative modeling approach improves forecasting accuracy and is tailored to the specific complexities of electrical power systems within low-carbon buildings. The model performance is rigorously evaluated using datasets from low-carbon buildings, including the Individual-Household-Electric-Power-Consumption (IHEPC) dataset from residential houses in Sceaux, Paris, and the Pennsylvania–New Jersey–Maryland (PJM) dataset. Beyond traditional benchmarks, our model undergoes comprehensive testing on data originating from ten diverse regions within the PJM dataset. The results demonstrate a significant reduction in forecasting error compared to existing state-of-the-art models. This research’s primary achievement lies in its ability to offer an efficient and adaptable solution tailored to real-world electrical power systems in low-carbon buildings, thus significantly contributing to the broader framework of modeling, simulation, and analysis within the field.

Keywords: power system; data driven; load forecasting; hybrid model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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