An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
- 1 January 2021
- journal article
- research article
- Published by Growing Science in International Journal of Industrial Engineering Computations
- Vol. 12 (4), 365-380
- https://doi.org/10.5267/j.ijiec.2021.5.005
Abstract
Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.Keywords
This publication has 14 references indexed in Scilit:
- ECJ at 20Published by Association for Computing Machinery (ACM) ,2019
- Domestic load management based on integration of MODE and AHP-TOPSIS decision making methodsSustainable Cities and Society, 2019
- Optimum design of a CCHP system based on Economical, energy and environmental considerations using GA and PSOInternational Journal of Industrial Engineering Computations, 2018
- Dynamic Appliances Scheduling in Collaborative MicroGrids SystemIEEE Transactions on Power Systems, 2016
- Energy management and planning in smart citiesRenewable and Sustainable Energy Reviews, 2015
- Integrated Management of Energy Resources in the Residential Sector Using Evolutionary ComputationAdvances in Environmental Engineering and Green Technologies, 2015
- A multi-objective genetic approach to domestic load scheduling in an energy management systemEnergy, 2014
- Optimization Models and Methods for Demand-Side Management of Residential Users: A SurveyEnergies, 2014
- Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actionsRenewable and Sustainable Energy Reviews, 2013
- A game-theoretic approach for optimal time-of-use electricity pricingIEEE Transactions on Power Systems, 2012