Higher penetration of renewable and smart home technologies at the residential level challenges grid stability as utility-customer interactions add complexity to power system operations. In response, short-term residential load forecasting has become an increasing area of focus. However, forecasting at the residential level is challenging due to the higher uncertainties involved. Recently deep neural networks have been leveraged to address this issue. This paper investigates the capabilities of a bidirectional long short-term memory (BiLSTM) and a convolutional neural network-based BiLSTM (CNN-BiLSTM) to provide a day ahead (24 hr.) forecasting at an hourly resolution while minimizing the root mean squared error (RMSE) between the actual and predicted load demand. Using a publicly available dataset consisting of 38 homes, the BiLSTM and CNN-BiLSTM models are trained to forecast the aggregated active power demand for each hour within a 24 hr. span, given the previous 24 hr. load data. The BiLSTM model achieved the lowest RMSE of 1.4842 for the overall daily forecast. In addition, standard LSTM and CNN-LSTM models are trained and compared with the BiLSTM architecture. The RMSE of BiLSTM is 5.60%, 2.85% and 2.60% lower than the LSTM, CNN-LSTM and CNN-BiLSTM models respectively. The source code of this work is available at https://github.com/Varat7v2/STLF-BiLSTM-CNNBiLSTM.git.

翻译：可再生能源与智能家居技术在住宅层面的高渗透率，因用户与电网互动增加了电力系统运行的复杂性，从而对电网稳定性构成挑战。为此，短期住宅负荷预测已成为日益关注的研究领域。然而，住宅层级负荷预测因涉及更高不确定性而具有挑战性。近年来，深度神经网络被用于解决该问题。本文研究了双向长短期记忆网络（BiLSTM）和基于卷积神经网络的BiLSTM（CNN-BiLSTM）在小时级分辨率下实现日前（24小时）预测的能力，目标是最小化实际与预测负荷需求之间的均方根误差（RMSE）。利用包含38户住宅的公开数据集，基于前24小时负荷数据训练BiLSTM和CNN-BiLSTM模型，以预测24小时时段内每小时的聚合有功功率需求。BiLSTM模型在整体日预测中取得了最低RMSE值1.4842。此外，本文训练了标准LSTM和CNN-LSTM模型并与BiLSTM架构进行对比。BiLSTM的RMSE比LSTM、CNN-LSTM和CNN-BiLSTM分别低5.60%、2.85%和2.60%。本工作源代码可从https://github.com/Varat7v2/STLF-BiLSTM-CNNBiLSTM.git获取。