sc 20(9): e4

Research Article

A Case Based Reasoning Coupling Multi-Criteria Decision Making with Learning and Optimization Intelligences: Application to Energy Consumption

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  • @ARTICLE{10.4108/eai.26-6-2018.162292,
        author={Naomi Dassi Tchomt\^{e} and Sohail Asghar and Nadeem Javaid and Paul Dayang and Duplex Elvis Houpa Danga and Dieudon\^{e} Lucien Bitom Oyono},
        title={A Case Based Reasoning Coupling Multi-Criteria Decision Making with Learning and Optimization Intelligences: Application to Energy Consumption},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={4},
        number={9},
        publisher={EAI},
        journal_a={SC},
        year={2019},
        month={12},
        keywords={AHP, CBR, Forecasting, PSO, Supervised Learning, Support Vector Regression},
        doi={10.4108/eai.26-6-2018.162292}
    }
    
  • Naomi Dassi Tchomté
    Sohail Asghar
    Nadeem Javaid
    Paul Dayang
    Duplex Elvis Houpa Danga
    Dieudoné Lucien Bitom Oyono
    Year: 2019
    A Case Based Reasoning Coupling Multi-Criteria Decision Making with Learning and Optimization Intelligences: Application to Energy Consumption
    SC
    EAI
    DOI: 10.4108/eai.26-6-2018.162292
Naomi Dassi Tchomté1,*, Sohail Asghar2, Nadeem Javaid2, Paul Dayang1, Duplex Elvis Houpa Danga1, Dieudoné Lucien Bitom Oyono3
  • 1: Mathematics and Computer Science, Faculty of Science, University of Ngaoundéré, Cameroon
  • 2: Department of Computer Science, COMSATS University Islamabad, Pakistan
  • 3: Faculty of Agronomy and Agricultural Sciences, University of Dschang, Cameroon
*Contact email: naomitdassi@gmail.com

Abstract

Optimization energy is a technique helpful to manage electricity consumption of home devices according to the electric system. CBR is used to predict consumption but lacks to be generic. This paper intends to design a more generic CBR approach by relying on various intelligences. The retrieve process includes four steps. The first step is weight evaluation of attributes based on AHP. The second step exploits an adapted cosine model for distance similarity. The third and fourth steps use k-Means and k-NN to identify the most similar cases. The reuse process is defined as a linear programming problem solved by PSO. During revise, an algorithm based on the reuse model and SVR, derives the revised solution. Experiments on a dataset of 1096 samples are made for forecasting energy electricity consumption. CBR revise process is 99.35% accurate, improving the reuse accuracy by 11%. The proposed architecture is a potential in energy management as well as for other prediction problems.