Gab-SSDS: An AI-Based Similar Days Selection Method for Load Forecast

Abstract

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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.

Keywords

• artificial neural networks
• effectiveness
• hybrid method
• STLF
• genetic algorithm
• tensorflow