Forecasting of Smart Meter Time Series Based on Neural Networks

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• 关键词：Smart meter ; Short ; term forecasting ; Artificial neural network ; Data preparation ; Power flow ; Big data analytics
• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10097
• 期：1
• 页码：10-21
• 丛书名：Data Analytics for Renewable Energy Integration
• ISBN：978-3-319-50947-1
• 卷排序：10097

文摘

In traditional power networks, Distribution System Operators (DSOs) used to monitor energy flows on a medium- or high-voltage level for an ensemble of consumers and the low-voltage grid was regarded as a black box. However, electric utilities nowadays obtain ever more precise information from single consumers connected to the low- and medium-voltage grid thanks to smart meters (SMs). This allows a previously unattainable degree of detail in state estimation and other grid analysis functionalities such as predictions. This paper focuses on the use of Artificial Neural Networks (ANNs) for accurate short-term load and Photovoltaic (PV) predictions of SM profiles and investigates different spatial aggregation levels. A concluding power flow analysis confirms the benefits of time series prediction to support grid operation. This study is based on the SM data available from more than 40,000 consumers as well as PV systems in the City of Basel, Switzerland.