{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:11:47Z","timestamp":1772766707188,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,3,10]],"date-time":"2017-03-10T00:00:00Z","timestamp":1489104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology of Taiwan","doi-asserted-by":"publisher","award":["MOST 104-2221-E-035-030"],"award-info":[{"award-number":["MOST 104-2221-E-035-030"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA) and Cuckoo Optimization Algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.<\/jats:p>","DOI":"10.3390\/info8010031","type":"journal-article","created":{"date-parts":[[2017,3,10]],"date-time":"2017-03-10T09:39:55Z","timestamp":1489138795000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms"],"prefix":"10.3390","volume":"8","author":[{"given":"Jeng-Fung","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shih-Kuei","family":"Lo","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quang","family":"Do","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Transport Technology, Hanoi 100000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1016\/j.rser.2011.08.014","article-title":"Energy models for demand forecasting\u2014A review","volume":"16","author":"Suganthia","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1007\/s00521-011-0778-0","article-title":"Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks","volume":"22","author":"Dogan","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s00521-012-1302-x","article-title":"The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test","volume":"24","author":"Erzin","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TPAMI.2008.88","article-title":"Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals","volume":"31","author":"Bhattacharya","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.3390\/en4081246","article-title":"Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach","volume":"4","author":"Kandananond","year":"2011","journal-title":"Energies"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ijepes.2010.08.008","article-title":"Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach","volume":"33","author":"Chang","year":"2011","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Feilat, E.A., and Bouzguenda, M. (2011, January 17\u201320). Medium-term load forecasting using neural network approach. Proceedings of the IEEE PES Conference on Innovative Smart Grid Technologies\u2014Middle East (ISGT Middle East), Jeddah, Saudi Arabia.","DOI":"10.1109\/ISGT-MidEast.2011.6220810"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"488","DOI":"10.3390\/en4030488","article-title":"A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems","volume":"4","author":"Amjady","year":"2011","journal-title":"Energies"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ijepes.2011.12.018","article-title":"PREDICT\u2014Decision support system for load forecasting and inference: A new undertaking for Brazilian power suppliers","volume":"38","author":"Santana","year":"2012","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1016\/j.amc.2006.08.094","article-title":"Forecasting electrical consumption by integration of Neural Network, time series and ANOVA","volume":"186","author":"Azadeh","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1016\/j.enconman.2008.01.035","article-title":"Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors","volume":"49","author":"Azadeh","year":"2008","journal-title":"Energy Convers. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1016\/j.epsr.2007.12.001","article-title":"Day-ahead price forecasting in restructured power systems using artificial neural networks","volume":"78","author":"Vahidinasab","year":"2008","journal-title":"Electr. Power Syst. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TPWRS.2008.2008606","article-title":"Applying wavelets to short-term load forecasting using PSO-based neural networks","volume":"24","author":"Bashir","year":"2009","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/34.107014","article-title":"On the problem of local minima in back-propagation","volume":"14","author":"Gori","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1016\/j.amc.2006.07.025","article-title":"A hybrid particle swarm optimization\u2013back-propagation algorithm for feed-forward neural network training","volume":"185","author":"Zhang","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_16","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley."},{"key":"ref_17","unstructured":"Kennedy, J., and Eberhart, R.C. (,  1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/3477.484436","article-title":"Ant system: Optimization by a colony of cooperating agents","volume":"26","author":"Dorigo","year":"1996","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous Spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","article-title":"GSA: A Gravitational Search Algorithm","volume":"179","author":"Rashedi","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_21","first-page":"2890","article-title":"Gravitational Search Algorithm for Economic Dispatch with Valve-Point Effects","volume":"5","author":"Duman","year":"2010","journal-title":"Int. Rev. Electr. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5508","DOI":"10.1016\/j.asoc.2011.05.008","article-title":"Cuckoo optimization algorithm","volume":"11","author":"Rajabioun","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/982354","article-title":"Parameter Optimization via Cuckoo Optimization Algorithm of Fuzzy Controller for Liquid Level Control","volume":"2013","author":"Balochian","year":"2013","journal-title":"J. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Deng, J. (2010, January 13\u201315). Energy Demand Estimation of China Using Artificial Neural Network. Proceedings of the 2010 Third International Conference on Business Intelligence and Financial Engineering, Hong Kong, China.","DOI":"10.1109\/BIFE.2010.18"},{"key":"ref_25","first-page":"87","article-title":"Forecasting Turkey\u2019s Energy Demand Using Artificial Neural Networks: Three Scenario Applications","volume":"11","author":"Hotunluoglu","year":"2011","journal-title":"Ege Acad. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.3390\/en7041935","article-title":"Short-Term Electrical Peak Demand Forecasting in a Large Government Building Using Artificial Neural Networks","volume":"7","author":"Grant","year":"2014","journal-title":"Energies"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4489","DOI":"10.3390\/en6094489","article-title":"Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment","volume":"6","author":"Aguiar","year":"2013","journal-title":"Energies"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ryu, S., Noh, J., and Kim, H. (2017). Deep Neural Network Based Demand Side Short Term Load Forecasting. Energies, 10.","DOI":"10.3390\/en10010003"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0893-6080(89)90003-8","article-title":"On the approximate realization of continuous mappings by neural networks","volume":"2","author":"Funahashi","year":"1989","journal-title":"Neural Netw."},{"key":"ref_30","unstructured":"Norgaard, M., Poulsen, N., and Hansen, L. (2000). Neural Networks for Modeling and Control of Dynamic Systems. A Practitioner\u2019s Handbook, Springer."},{"key":"ref_31","unstructured":"Caruana, R., Lawrence, S., and Giles, C. Overfitting in neural networks: Backpropagation, conjugate gradient, and early stopping. Available online: https:\/\/papers.nips.cc\/paper\/1895-overfitting-in-neural-nets-backpropagation-conjugate-gradient-and-early-stopping.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superposition of a sigmoid function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control Signals Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/0893-6080(90)90005-6","article-title":"Universal Approximation of an unknown Mapping and its Derivatives Using Multilayer Feed forward Networks","volume":"3","author":"Hornik","year":"1990","journal-title":"Neural Netw."},{"key":"ref_34","first-page":"163","article-title":"Applying Neural Networks for Simplified Data Encryption Standard (SDES) Cipher System Cryptanalysis","volume":"9","author":"Alallayah","year":"2012","journal-title":"Int. Arab J. Inf. Technol."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/1\/31\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:30:10Z","timestamp":1760207410000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/1\/31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,10]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["info8010031"],"URL":"https:\/\/doi.org\/10.3390\/info8010031","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,10]]}}}