Hyperparameter optimization results.

There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, and networked transportation. However, there are concerns that these initiatives could burden the electricity grid due to increased demand. Hence, the need for accurate short-term load forecasting is pivotal for the efficient planning, operation, and control of the grid and associated power systems. This study presents robust models for forecasting half-hourly and hourly loads in the UK’s power system. The work leverages machine learning techniques such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) to develop robust prediction models using the net imports dataset from 2010 to 2020. The models were evaluated based on metrics like Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and the Correlation of Determination (R2). For half-hourly forecasts, SVR performed best with an R-value of 99.85%, followed closely by GPR and ANN. But, for hourly forecasts, ANN led with an R-value of 99.71%. The findings affirm the reliability and precision of machine learning methods in short-term load forecasting, particularly highlighting the superior accuracy of the SVR model for half-hourly forecasts and the ANN model for hourly forecasts.全球范围内正积极推广电动汽车（Electric Vehicles，EVs），因其有望在能源转型中发挥关键作用。这些举措凸显了电动出行范式的重要性，其本质是可再生能源、智能技术与网络化交通的有机融合。然而，人们担忧此类项目会因电力需求增长而给电网带来负荷压力。因此，精准的短期负荷预测对于电网及相关电力系统的高效规划、运行与调控至关重要。本研究针对英国电力系统的半小时与小时级负荷预测，构建了鲁棒性较强的预测模型。本研究借助支持向量回归（Support Vector Regression，SVR）、人工神经网络（Artificial Neural Networks，ANN）以及高斯过程回归（Gaussian Process Regression，GPR）等机器学习方法，基于2010年至2020年的净进口数据集开发了鲁棒预测模型。模型的评估采用了均方根误差（Root Mean Square Error，RMSE）、平均绝对预测误差（Mean Absolute Prediction Error，MAPE）、平均绝对偏差（Mean Absolute Deviation，MAD）以及决定系数（Correlation of Determination，R²）等指标。在半小时级负荷预测中，支持向量回归表现最优，决定系数达99.85%，紧随其后的是高斯过程回归与人工神经网络。而在小时级负荷预测中，人工神经网络表现最佳，决定系数为99.71%。研究结果证实了机器学习方法在短期负荷预测中的可靠性与精准性，尤其凸显了支持向量回归模型在半小时级负荷预测中的优异精度，以及人工神经网络模型在小时级负荷预测中的出色表现。