This research endeavors to create an advanced machine learning model designed for the prediction of household electricity consumption. It leverages a multidimensional time-series dataset encompassing energy consumption profiles, customer characteristics, and meteorological information. A comprehensive exploration of diverse deep learning architectures is conducted, encompassing variations of recurrent neural networks (RNNs), temporal convolutional networks (TCNs), and traditional autoregressive moving average models (ARIMA) for reference purposes. The empirical findings underscore the substantial enhancement in forecasting accuracy attributed to the inclusion of meteorological data, with the most favorable outcomes being attained through the application of time-series convolutional networks. Additionally, an in-depth investigation is conducted into the impact of input duration and prediction steps on model performance, emphasizing the pivotal role of selecting an optimal duration and number of steps to augment predictive precision. In summation, this investigation underscores the latent potential of deep learning in the domain of electricity consumption forecasting, presenting pragmatic methodologies and recommendations for household electricity consumption prediction.

中文翻译：

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通过深度学习进行能源消耗预测

这项研究致力于创建一种先进的机器学习模型，旨在预测家庭用电量。它利用包含能源消耗概况、客户特征和气象信息的多维时间序列数据集。对多种深度学习架构进行了全面探索，包括循环神经网络 (RNN)、时间卷积网络 (TCN) 和传统自回归移动平均模型 (ARIMA) 的变体，以供参考。实证结果强调了由于纳入气象数据而大大提高了预测准确性，并且通过应用时间序列卷积网络获得了最有利的结果。此外，还深入研究了输入持续时间和预测步骤对模型性能的影响，强调选择最佳持续时间和步骤数量对提高预测精度的关键作用。总之，这项研究强调了深度学习在用电量预测领域的潜在潜力，为家庭用电量预测提出了实用的方法和建议。