Research Article
A Case Based Reasoning Coupling Multi-Criteria Decision Making with Learning and Optimization Intelligences: Application to Energy Consumption
@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
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.
Copyright © 2019 Naomi Dassi Tchomté et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
