The flexibility in electricity consumption and production in communities of residential buildings, including those with renewable energy sources and energy storage (a.k.a., prosumers), can effectively be utilized through the advancement of short-term demand response mechanisms. It is known that flexibility can further be increased if demand response is performed at the level of communities of prosumers, since aggregated groups can better coordinate electricity consumption. However, the effectiveness of such short-term optimization is highly dependent on the accuracy of electricity load forecasts both for each building as well as for the whole community. Structural variations in the electricity load profile can be associated with different exogenous factors, such as weather conditions, calendar information and day of the week, as well as user behavior. In this paper, we review a wide range of electricity load forecasting techniques, that can provide significant assistance in optimizing load consumption in prosumer communities. We present and test artificial intelligence (AI) powered short-term load forecasting methodologies that operate with black-box time series models, such as Facebook's Prophet and Long Short-term Memory (LSTM) models; season-based SARIMA and smoothing Holt-Winters models; and empirical regression-based models that utilize domain knowledge. The integration of weather forecasts into data-driven time series forecasts is also tested. Results show that the combination of persistent and regression terms (adapted to the load forecasting task) achieves the best forecast accuracy.

翻译：住宅建筑社区（包括配备可再生能源和储能系统的社区，即产消者）中的电力消费与生产灵活性，可通过短期需求响应机制的进步得到有效利用。已知若在产消者社区层面实施需求响应，由于聚合群体能更好地协调电力消费，灵活性可进一步提升。然而，此类短期优化的有效性高度依赖于对每栋建筑及整个社区电力负荷预测的准确性。电力负荷曲线的结构性变化可能与不同外生因素相关，例如天气条件、日历信息和星期特征，以及用户行为。本文综述了多种电力负荷预测技术，这些技术可为优化产消者社区的负荷消费提供重要支撑。我们提出并测试了基于人工智能的短期负荷预测方法，这些方法采用黑箱时间序列模型（如Facebook的Prophet和长短期记忆网络模型）、基于季节性的SARIMA和平滑指数平滑Holt-Winters模型，以及利用领域知识的经验回归模型。同时测试了将天气预报集成到数据驱动时间序列预测中的效果。结果表明，结合持续性项与回归项（针对负荷预测任务进行适配）可实现最优预测精度。