A multi-objective home energy management system based on internet of things and optimization algorithms

Abstract This study presents a new optimal method for home energy management system based on the internet of things. The method is a multi-objective optimization method that considers two main purposes including energy consumption cost and user satisfaction. The method is designed under the environment of the smart grid. Generally, the impact of the users in the system efficiency in terms of energy cost saving is significant. This reason makes residential users participate in household appliances management. The optimization algorithm is based on a new improved version of the butterfly algorithm for increasing the convergence speed. IoT system is based on ZigBee which is known as the lowest consumption among different wireless technologies. The household employs based on a sample user scenario with different appliances. Using Multi-objective optimization gives fragmented energy consumption. The results of Multi-objective optimization are also compared with PSO-based and BOA-based algorithms to show the proposed method's effectiveness. Simulation results are compared by the normal home energy management system to declare the system efficiency.

[1]  Sung-Kwan Joo,et al.  Smart heating and air conditioning scheduling method incorporating customer convenience for home energy management system , 2013, IEEE Transactions on Consumer Electronics.

[2]  Noradin Ghadimi,et al.  High step-up interleaved dc/dc converter with high efficiency , 2020 .

[3]  Noradin Ghadimi,et al.  Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system , 2016, Journal of Intelligent & Fuzzy Systems.

[4]  Mehdi Hosseinzadeh,et al.  A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm , 2018, Eng. Appl. Artif. Intell..

[5]  Miao Yu,et al.  Research on intelligent city energy management based on Internet of things , 2018, Cluster Computing.

[6]  Robert B. Blair,et al.  Butterfly diversity and human land use: Species assemblages along an urban gradient , 1997 .

[7]  Hoang Dung Vu,et al.  Energy Optimization of an In-Service Commercial Building Chiller Plant via Extremum Seeking Control , 2018 .

[8]  Tom Molinski,et al.  PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data , 2012, IEEE Transactions on Smart Grid.

[9]  Noradin Ghadimi,et al.  A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets , 2017, J. Intell. Fuzzy Syst..

[10]  Selcuk Aslan,et al.  Discovery of conserved regions in DNA sequences by Artificial Bee Colony (ABC) algorithm based methods , 2018, Natural Computing.

[11]  Dae-Man Han,et al.  Smart home energy management system using IEEE 802.15.4 and zigbee , 2010, IEEE Transactions on Consumer Electronics.

[12]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[13]  Ning Lu,et al.  Appliance Commitment for Household Load Scheduling , 2011, IEEE Transactions on Smart Grid.

[14]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[15]  Alireza Nouri,et al.  Planning in Microgrids With Conservation of Voltage Reduction , 2018, IEEE Systems Journal.

[16]  Noradin Ghadimi,et al.  Multi-objective energy management in a micro-grid , 2018, Energy Reports.

[17]  Ayda Darvishan,et al.  Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming , 2018, Applied Thermal Engineering.

[18]  Poria Fajri,et al.  Optimal management of residential energy storage systems in presence of intermittencies , 2020 .

[19]  Tsair-Fwu Lee,et al.  Optimization and Implementation of a Load Control Scheduler Using Relaxed Dynamic Programming for Large Air Conditioner Loads , 2008, IEEE Transactions on Power Systems.

[20]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[21]  B Hussain,et al.  Impact studies of distributed generation on power quality and protection setup of an existing distribution network , 2010, SPEEDAM 2010.

[22]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[23]  OVEIS ABEDINIA,et al.  A new metaheuristic algorithm based on shark smell optimization , 2016, Complex..

[24]  Mehmet Tastan Internet of Things based Smart Energy Management for Smart Home , 2019, KSII Trans. Internet Inf. Syst..

[25]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[26]  Shuhui Li,et al.  An Optimal and Learning-Based Demand Response and Home Energy Management System , 2016, IEEE Transactions on Smart Grid.

[27]  Anzar Mahmood,et al.  Prosumer based energy management and sharing in smart grid , 2018 .

[28]  Sana Shuja,et al.  IoT-Based Secure Embedded Scheme for Insulin Pump Data Acquisition and Monitoring , 2018, ArXiv.

[29]  Jafar Adabi,et al.  A hierarchical energy management system for multiple home energy hubs in neighborhood grids , 2020 .

[30]  Chaker Aloui,et al.  Sectoral energy consumption by source and output in the U.S.: New evidence from wavelet-based approach , 2018 .

[31]  Simon Fong,et al.  Smart Power Management Internet of Things System with 5G and LoRa Hybrid Wireless Networks , 2019 .

[32]  Taryudi,et al.  Iot-based Integrated Home Security and Monitoring System , 2018, Journal of Physics: Conference Series.

[33]  Dawei Gao,et al.  An implementation of intelligent substation monitoring system based on Internet of things , 2019, Journal of Computational Methods in Sciences and Engineering.

[34]  Francesco Palmieri,et al.  Multi-layer cloud architectural model and ontology-based security service framework for IoT-based smart homes , 2018, Future Gener. Comput. Syst..

[35]  Marja Alastalo Eurostat: Making Europe Commensurate and Comparable , 2018 .

[36]  Karzan Wakil,et al.  Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach , 2019 .

[37]  Kyung-Bin Song,et al.  An Optimal Power Scheduling Method for Demand Response in Home Energy Management System , 2013, IEEE Transactions on Smart Grid.

[38]  Haiguo Tang,et al.  A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting , 2018, Adv. Eng. Informatics.

[39]  G. Cheng,et al.  On the efficiency of chaos optimization algorithms for global optimization , 2007 .

[40]  Kamaruzzaman Sopian,et al.  Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source , 2018, Renewable Energy.

[41]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[42]  P. Siano,et al.  Iot-based smart cities: A survey , 2016, 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC).

[43]  Noradin Ghadimi,et al.  The price prediction for the energy market based on a new method , 2018 .

[44]  Noradin Ghadimi,et al.  A new prediction model of battery and wind-solar output in hybrid power system , 2019, J. Ambient Intell. Humaniz. Comput..

[45]  Noradin Ghadimi,et al.  Robust optimization based optimal chiller loading under cooling demand uncertainty , 2019, Applied Thermal Engineering.

[46]  Jun Liu,et al.  An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles , 2020 .

[47]  Noradin Ghadimi,et al.  PSO Based Fuzzy Stochastic Long-Term Model for Deployment of Distributed Energy Resources in Distribution Systems With Several Objectives , 2013, IEEE Systems Journal.

[48]  Satvir Singh,et al.  Butterfly optimization algorithm: a novel approach for global optimization , 2018, Soft Computing.

[49]  Mouna Rekik,et al.  A collaborative energy management among plug-in electric vehicle, smart homes and neighbors’ interaction for residential power load profile smoothing , 2020 .

[50]  Noradin Ghadimi,et al.  Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods , 2016, Complex..

[51]  Hadi Zayandehroodi,et al.  A New Formulation to Reduce the Number of Variables and Constraints to Expedite SCUC in Bulky Power Systems , 2019 .

[52]  Mehdi Bagheri,et al.  Multi-objective Shark Smell Optimization for Solving the Reactive Power Dispatch Problem , 2018, 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[53]  Noradin Ghadimi Genetically tuning of lead-lag controller in order to control of fuel cell voltage , 2012 .

[54]  Wei Wang,et al.  Electricity load forecasting by an improved forecast engine for building level consumers , 2017 .

[55]  Dongmin Yu,et al.  Reliability constraint stochastic UC by considering the correlation of random variables with Copula theory , 2019, IET Renewable Power Generation.

[56]  Bangyin Liu,et al.  Smart energy management system for optimal microgrid economic operation , 2011 .

[57]  Karzan Wakil,et al.  RETRACTED: Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory , 2018, Solar Energy.

[58]  Qing Meng,et al.  A Single-Phase Transformer-Less Grid-Tied Inverter Based on Switched Capacitor for PV Application , 2020, Journal of Control, Automation and Electrical Systems.

[59]  Mohammad Ghiasi,et al.  Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve , 2019 .