Day-ahead aggregated load forecasting based on household smart meter data

Smart meters provide much energy consumption information at the residential level, making it possible to improve short-time load forecasting accuracy by identifying more specific load patterns for each consumer type. This paper proposes a day-ahead load forecasting approach that uses the smart meter data aggregated by residential customers' power consumption characteristics. First, the long-term trend information and daily fluctuation information are extracted from the residential load time series. According to the load characteristics reflected by the daily load fluctuation information, the residential consumers are clustered into several groups using the K-means algorithm. The non-linear autoregressive neural network is used to forecast each cluster of consumers to capture their specific load patterns. Finally, the aggregated load at the system level is obtained by combining each cluster's forecasting results. The proposed method's forecasting performance is evaluated on the data from Irish household customers' actual smart meters. The outcomes show that the suggested approach can improve forecasting performance under a specific clustering scale. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper proposes a day-ahead load forecasting approach that uses smart meter data aggregated by residential customers' power consumption characteristics. The approach improves forecasting accuracy by identifying specific load patterns for each consumer type. The method involves extracting long-term trend and daily fluctuation information, clustering residential consumers using the K-means algorithm, and forecasting each cluster's load patterns using a non-linear autoregressive neural network.