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A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption

  • Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results. This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans 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 accu-racy comparing to the existing models. 2018 American Institute of Chemical Engineers Environ Prog, 2018 https://doi.org/10.1002/ep.12934

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Document Type:Preprint
Author:Postdoc Amir MosaviORCiD, Professor Mehrnoosh Torabi, Prof Sattar Hashemi, Prof Mahmoud Reza Saybani, Prof Shahaboddin Shamshirband
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.3755Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20180907-37550Cite-Link
Date of Publication (online):2018/06/27
Date of first Publication:2018/06/27
Release Date:2018/09/07
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Bauhaus-Institut für zukunftsweisende Infrastruktursysteme
Tag:Electric Energy Consumption; Machine Learning; Prediction; artificial neural networks (ANN); clustering; forecasting; support vector machine (SVM)
GND Keyword:Data Mining
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:54 Informatik
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)
This is the pre-peer reviewed version of the following article: https://onlinelibrary.wiley.com/doi/10.1002/ep.12934, which has been published in final form at 
https://doi.org/10.1002/ep.12934. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.