Energy load forecasting is essential to effectively optimise the use of energy for the purpose of smart grid operation. In this work, a focus is placed on Short-Term Load Forecasts (STLF) using measured heat pump data from the UK utilising the Long Short-Term Memory (LSTM) method. A load forecasting program is developed to forecast the electricity consumption of heat pumps based on historical data. LSTM is compared to Backpropagation Neural Network (BPNN), Seasonal Autoregressive Integrated Moving Average (SARIMA), Random Forest (RF) and Holt-Winters Exponential Smoothing (ES). The best average Mean Absolute Percent Error (MAPE) of 1.59% is achieved for the test data set by LSTM, which outperforms other models compared and a number of models presented in papers in the literature. By conducting the Wilcoxon signed-rank test, it is proven that the prediction accuracy improvement of LSTM compared to other models is statistically significant with the exception when comparing with RF with the training data. The impact of data aggregation resolution to forecast accuracy is investigated. The proposed LSTM model performs best on 1-h resolution data compared with other resolution time periods. The results of the LSTM model are integrated with Home Energy Management Systems (HEMS). The electricity costs of the HEMS are compared when using forecasted and measured heat pump data respectively. The results indicate an average percentage difference of 2.06% between the predicted and the actual electricity cost of HEMS for the test data. Furthermore, this paper presents a combined neural network and Particle Swarm Optimisation (PSO) model to develop an optimised energy management system while proving the effectiveness of the LSTM algorithm.

能源负荷预测对于有效优化能源使用以实现智能电网运行至关重要。在这项工作中，重点是使用来自英国的测量热泵数据利用长期短期记忆 (LSTM) 方法进行短期负荷预测 (STLF)。开发了一个负荷预测程序，以根据历史数据预测热泵的电力消耗。LSTM 与反向传播神经网络 (BPNN)、季节性自回归综合移动平均 (SARIMA)、随机森林 (RF) 和 Holt-Winters 指数平滑 (ES) 进行了比较。LSTM 的测试数据集实现了 1.59% 的最佳平均平均绝对百分比误差 (MAPE)，其性能优于其他比较模型和文献论文中提出的许多模型。通过进行 Wilcoxon 符号秩检验，事实证明，与其他模型相比，LSTM 的预测精度提高在统计上是显着的，除了与 RF 与训练数据进行比较时。研究了数据聚合分辨率对预测准确性的影响。与其他分辨率时间段相比，所提出的 LSTM 模型在 1 小时分辨率数据上表现最佳。LSTM 模型的结果与家庭能源管理系统 (HEMS) 相结合。分别使用预测和测量的热泵数据比较 HEMS 的电力成本。结果表明，对于测试数据，HEMS 的预测电力成本与实际电力成本之间的平均百分比差异为 2.06%。此外，