Intelligent Energy Management and Prediction of Micro Grid Operation Based on Machine Learning Algorithms and genetic algorithm

Mohamed Ali Elweddad, Muhammet Tahir GUNESER

Abstract


Microgrid energy management has become critically important due to inefficient power use in the residential sector. High energy consumption necessitates developing a strategy to manage the power flow efficiently. For this purpose, this work has been divided into two phases: The first is the "ON/OFF" operation, which has been executed using a genetic algorithm for the hybrid system, including diesel generator, solar photovoltaic (PV), wind turbine, and battery. Then, in the second phase, the output results were used as input in three algorithms to predict load and supply dispatch one month ahead. This study has two objectives; the first is to decide which energy source should meet the load one month ahead. The second is to compare the outcomes of machine-learning techniques, namely Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbours (KNN), to determine the one that performs the best. The results indicated that the DT technique has the best performance in the application of classification with an accuracy of 100%. The findings also show that the RF approach gives acceptable results with an accuracy of up to 98%, and the KNN algorithm was the worst one in terms of accuracy with a value of 28%.

Keywords


Renewable energy; Power management; Load classification; Machine learning algorithms.

Full Text:

PDF

References


M . Talha, M. S .Saeed., G. Mohiuddin, “optimization in home energy management system using artificial fish swarm and genetic algorithms”. International conference on intelligent networking and collaborative systems. pp. 203-213.August 2017.

H.Wang, N.Zhang, Du, E., Yan, J., Y. “A comprehensive review for wind, solar, and electrical load forecasting methods”. Global Energy Interconnection, 5(1), 9-30, May 2022.

H. Aly,“A proposed intelligent short-term load forecasting hybrid models of ANN, WNN, and KF based on clustering techniques for smart grid”. Electric Power Systems Research, 182, 106191,May (2020).

H. Aly, “An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting. Sustainable Energy Technologies and Assessments”, 41,100802. October 2020.

A.Kaabeche,R.Ibtiouen,” Techno-economic optimization of hybrid photovoltaic/wind/diesel/battery generation in a stand-alone power system”', Solar Energy 103, pp. 171–182, May 2014.

L.,N.An, T. Tuan, “Dynamic programming for optimal energy management of hybrid wind–PV–diesel–battery. Energies”, 11(11), 3039, Otober 2018.

H. Tazvinga, X .Xia, J. Zhang, “Minimum cost solution of photovoltaic–diesel–battery hybrid power systems for remote consumers”. Solar Energy, 96, 292-299.? Otober 2013.

M.Ouassaid,M. Maaroufi, “Smart home appliances modeling and simulation for energy consumption profile development: Application to Moroccan real environment case study”. In 2016 International Renewable and Sustainable Energy Conference (IRSEC) (pp. 1050 1055). IEEE.? November 2016.

Y.Sawle, S.Gupta, A. Bohre, “Socio-techno-economic design of hybrid renewable energy system using optimization techniques”. Renewable energy, 119, 459-472,April 2014.

J.Dulout, A.Hernández., A.Anvari-A. “Optimal scheduling of a battery-based energy storage system for a microgrid with high penetration of renewable sources”. In ELECTRIMACS (pp. 1-6). IMACS.? Energy, 2013, 96, pp. 292–299.?Julay 2017.

M.Guo, W.Wang, R. Chen,. “Renewable hybrid energy system scheduling strategy considering demand response”. Sustainable Energy Technologies and Assessments, 52, 102247, August 2022.

M.Kiptoo, O. Adewuyi, M. Lotfy,”Harnessing demand-side management benefit toward achieving a 100% renewable energy microgrid”. Energy Reports, 6, 680-685.? February 2020.

O.Ogunmodede, K.Anderson, D. Cutler, A. Newman, “Optimizing design and dispatch of a renewable energy system”. Applied Energy, 287, 116527? (2021, April 2021.

S.Zheng, G. Huang, A. Lai, “Techno-economic performance analysis of synergistic energy sharing strategies for grid-connected prosumers with distributed battery storages”. Renewable Energy, 178, 1261-1278. November 2021.

D.Solyali, “A comparative analysis of machine learning approaches for short-/long-term electricity load forecasting in Cyprus”. Sustainability, 12(9), 3612.? April 2020.

H.Çevik, M. Çunka?, “Short-term load forecasting using fuzzy logic and ANFIS|”. Neural Computing and Applications, 26(6), 1355-1367.? January 2015.

J.Wasilewski, D. Baczynski, “Short-term electric energy production forecasting at wind power plants in Pareto-optimality context”. Renewable and Sustainable Energy Reviews, 69, 177-187?. March 2017.

T.Burianek, J.Stuchly, S. Misak, “Solar power production forecasting based on the recurrent neural network”. In Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA (pp. 195-204).? January 2015.

Y.Zhang, M. Beaudin, H. Zareipour, D. Wood,” Forecasting solar photovoltaic power production at the aggregated system level”. In 2014 North American Power Symposium (NAPS) (pp. 1-6). IEEE.? November 2014.

M.Sunny, M. Kabir, I.T. Naheen, “Residential energy management”: A machine learning perspective. 2020 IEEE Green Technologies Conference (GreenTech) (pp. 229-234). IEEE.? December 2020.

G.Nalcaci, A. Özmen, G. Weber, “Long-term load forecasting: models based on MARS, ANN and LR methods”. Central European Journal of Operations Research, 27(4), 1033-104, March 2018.

D.G.Rosero, N.Díaz, C. Trujillo, “Cloud and machine learning experiments applied to the energy management in a microgrid cluster”. Applied Energy, 304, 117770. December 2021.

A.Ahmad, M. Hassan, A. Abdullah,. “A review on applications of ANN and SVM for building electrical energy consumption forecasting”. Renewable and Sustainable Energy Reviews, 33, 102-109 , May 2014.

P.Khan, Y.Byun, S. Lee, “learning-based approach to predict the energy consumption of renewable and nonrenewable power sources”. Energies, 13(18), 4870, September 2020.

A.Pham, N. Ngo, T. Truong, “Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability”. Journal of Cleaner Production, 260, 121082.? July 2020.

J.Jupin, T.Sutikno, M. Ismail,” Review of the machine learning methods in the classification of a phishing attack”. Bulletin of Electrical Engineering and Informatics, 8(4), 1545-1555. December 2019.

L. Dong, P. Kang,” Prediction of rockburst classification using Random Forest” Transactions of Nonferrous Metals Society of China, 23(2), 472-477, February 2013.

B.Charbuty, A.Abdulazeez, “Classification based on decision tree algorithm for machine learning”. Journal of Applied Science and Technology Trends, 2(01), 20-28,April 2021.

Hossin, Mohammad, and Md Nasir Sulaiman. et al. (2015) A review on evaluation metrics for data classification evaluations." International journal of data mining & knowledge management process (5).2 March 2015.




DOI (PDF): https://doi.org/10.20508/ijrer.v12i4.13418.g8563

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2025; 

h=35,

Average citation per item=6.59

Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43

Category Quartile:Q4