文章基本信息

标题：Gab-SSDS: An AI-Based Similar Days Selection Method for Load Forecast
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作者：Zoran Janković ; Boban Vesin ; Aleksandar Selakov 等
期刊名称：Frontiers in Energy Research
电子版ISSN：2296-598X
出版年度：2022
卷号：10
DOI：10.3389/fenrg.2022.844838
语种：English
出版社：Frontiers Media S.A.
摘要：The important, while mostly underestimated, step in the process of short-term load forecasting–STLF is the selection of similar days. Similar days are identified based on numerous factors, such as weather, time, electricity prices, geographical conditions and consumers’ types. However, those factors influence the load differently within different circumstances and conditions. To investigate and optimise the similar days selection process, a new forecasting method, named Genetic algorithm-based–smart similar days selection method–Gab-SSDS, has been proposed. The presented approach implements the genetic algorithm selecting similar days, used as input parameters for the STLF. Unlike other load forecasting methods that use the genetic algorithm only to optimise the forecasting engine, authors suggest additional use for the input selection phase to identify the individual impact of different factors on forecasted load. Several experiments were executed to investigate the method’s effectiveness, the forecast accuracy of the proposed approach and how using the genetic algorithm for similar days selection can improve traditional forecasting based on an artificial neural network. The paper reports the experimental results, which affirm that the use of the presented method has the potential to increase the forecast accuracy of the STLF.