Abstract
This paper presents a hybrid approach to predict the electric energy usage of weather‐sensitive loads. The presented method utilizes the clustering paradigm along with ANN and SVM approaches for accurate short‐term prediction of electric energy usage, using weather data. Since the methodology being invoked in this research is based on CRISP data mining, data preparation has received a great deal of attention in this research. Once data pre‐processing was done, the underlying pattern of electric energy consumption was extracted by the means of machine learning methods to precisely forecast short‐term energy consumption. The proposed approach (CBA‐ANN‐SVM) was applied to real load data and resulting higher accuracy comparing to the existing models. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 66–76, 2019
Highlights
Today, the electric power industries play an essential role in the orchestration and progress of fundamental infrastructures of countries
This research invoked methodology based on Cross Industry Standard Process (CRISP) data mining and used support vector machine (SVM), artificial neural networks (ANNs), and CBA-ANN-SVM to predict short-term electrical energy demand of Bandarabbas
After consulting with experts in the field of power con-sumption and plotting daily power consumption for each week, this research showed that official holidays and week-ends have impact on the power consumption
Summary
The electric power industries play an essential role in the orchestration and progress of fundamental infrastructures of countries. Electric energy transaction is always attributed to a certain amount (in MWH) which should be delivered within a certain period. Such a period may be fixed on the basis of the country or the area in which the market is located. There are different ways of implementing data mining tasks; one powerful method is Cross Industry Standard Process (CRISP) for Data Mining. SVM is a distinguished supervised classifier and based on statistical learning theory [31]. This method maps the original input space through multiple dimensional space [32]. SVM has a significant number of applications and methods in the field of load forecasting because it is highly generalizable and a sparse solution repre-sentation; it has no problems with local minima [4]
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