Nearest neighbors with incremental learning for real-time forecasting of electricity demand

Laura Melgar-García, David Gutiérrez-Avilés, Cristina Rubio-Escudero, A. T. Lora
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Abstract

Electricity demand forecasting is very useful for the different actors involved in the energy sector to plan the supply chain (generation, storage and distribution of energy). Nowadays energy demand data are streaming data coming from smart meters and has to be processed in real-time for more efficient demand management. In addition, this kind of data can present changes over time such as new patterns, new trends, etc. Therefore, real-time forecasting algorithms have to adapt and adjust to online arriving data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand forecasting in real-time. The proposed algorithm generates a prediction model based on the K-nearest neighbors algorithm, which is incrementally updated as online data arrives. Both time-frequency and error threshold based model updates have been evaluated. Results using Spanish electricity demand data with a ten-minute sampling frequency rate are reported, reaching 2% error with the best prediction model obtained when the update is daily.
最近的邻居,增量学习,实时预测电力需求
电力需求预测对于参与能源部门的不同参与者规划供应链(发电、储存和分配能源)非常有用。如今,能源需求数据是来自智能电表的流数据,必须实时处理才能更有效地进行需求管理。此外,这类数据可以随着时间的推移呈现变化,如新的模式、新的趋势等。因此,实时预测算法必须适应和调整在线到达的数据,以便提供及时准确的响应。本文提出了一种新的实时电力需求预测算法。该算法基于k近邻算法生成预测模型,该模型随着在线数据的到来而增量更新。对基于时频和误差阈值的模型更新进行了评估。采用西班牙电力需求数据,以10分钟的采样频率进行报告,当每日更新时,获得的最佳预测模型误差达到2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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