Validation of a 24-hour-ahead Prediction model for a Residential Electrical Load under diverse climate

TL;DR

This paper introduces a global 24-hour-ahead residential electrical load prediction model that performs accurately across diverse climates and limited datasets, outperforming existing methods and enhancing energy management efficiency worldwide.

Contribution

The paper presents a novel, climate-agnostic prediction model capable of reliable performance with minimal data, validated across different regions and seasons.

Findings

Achieves 8.0% MAPE in Ireland

Achieves 4.0% MAPE in Vietnam

Outperforms state-of-the-art models

Abstract

Accurate household electrical energy demand prediction is essential for effectively managing sustainable Energy Communities. Integrated with the Energy Management System, these communities aim to optimise operational costs. However, most existing forecasting models are region-specific and depend on large datasets, limiting their applicability across different climates and geographical areas. These models often lack flexibility and may not perform well in regions with limited historical data, leading to inaccurate predictions. This paper proposes a global model for 24-hour-ahead hourly electrical energy demand prediction that is designed to perform effectively across diverse climate conditions and datasets. The model's efficiency is demonstrated using data from two distinct regions: Ireland, with a maritime climate and Vietnam, with a tropical climate. Remarkably, the model achieves high accuracy even with a limited dataset spanning only nine months. Its robustness is further validated across different seasons in Ireland (summer and winter) and Vietnam (dry and wet). The proposed model is evaluated against state-of-the-art machine learning and deep learning methods. Simulation results indicate that the model consistently outperforms benchmark models, showcasing its capability to provide reliable forecasts globally, regardless of varying climatic conditions and data availability. This research underscores the model's potential to enhance the efficiency and sustainability of Energy Communities worldwide. The proposed model achieves a Mean Absolute Percentage Error of 8.0% and 4.0% on the full Irish and Vietnamese datasets.