面向产消者社区的电力负荷人工智能预测
AI-Powered Predictions for Electricity Load in Prosumer Communities
AI总结：
本文针对产消者社区短期需求响应中的负荷预测问题，测试了Prophet、LSTM、SARIMA、Holt-Winters及经验回归等多种人工智能预测方法，发现结合持久项与回归项的方法预测精度最佳。
摘要：
住宅建筑社区（包括配备可再生能源和储能设备的建筑，即产消者）在电力消费和生产方面的灵活性，可以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执行需求响应，灵活性可进一步提升，因为聚合群体能更好地协调电力消费。然而，此类短期优化的有效性高度依赖于每栋建筑及整个社区的电力负荷预测精度。电力负荷曲线中的结构性变化可能与不同外生因素相关，如天气条件、日历信息和星期几，以及用户行为。本文回顾了广泛的电力负荷预测技术，这些技术可为优化产消者社区的负荷消费提供重要帮助。我们提出并测试了人工智能驱动的短期负荷预测方法，包括使用黑箱时间序列模型，如Facebook的Prophet和长短期记忆（LSTM）模型；基于季节性的SARIMA和平滑Holt-Winters模型；以及利用领域知识的经验回归模型。此外，还测试了将天气预报集成到数据驱动的时间序列预测中的效果。结果表明，结合持久项和回归项（针对负荷预测任务进行调整）的方法实现了最佳的预测精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
