{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:45:43Z","timestamp":1777704343451,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T00:00:00Z","timestamp":1586304000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,4,30]]},"abstract":"<jats:p>\n                    \u00a0Energy uncertainty and ecological pressures have contributed to a high volatility in energy demand and consumption. The building sector accounts for 30 to 40% of the total global energy consumption. There is a high demand for novel techniques and viable energy strategies for reducing energy consumption in this domain. Energy prediction models have the potential to play a pivotal role in optimising energy consumption. The proposed work presents a new and accurate Energy Demand Prediction (EDP) model for large buildings. This approach leverages the Random Neural Network (RNN) prediction methodology. The proposed RNN-based EDP is compared with traditional Artificial Neural Network (ANN), Support Vector Machine (SVM) and linear regression models. A large building is modelled and simulated for one year in the Integrated Environment Solutions Virtual Environment (IES-VE). Several data inputs such as air temperature, internal gain and the number of people (occupancy) are calculated from IES-VE model and provided to traditional ANN and the proposed RNN predictor. A number of test parameters such as Root Mean Square (RMSE), Normalized Root Mean Square (N-RMSE), Mean Absolute Percentage Error (MAPE) and\n                    <jats:italic>R<\/jats:italic>\n                    provide the proposed RNN model with higher accuracy over the traditional ANN, SVM and linear regression. The proposed RNN predictor provides approximately half of the error of the ANN model. The traditional ANN model gives higher error values of 2.07\u00d7, 1.83\u00d7 and 2.35\u00d7 for RMSE, NRMSE and MAPE, respectively as compared to the proposed RNN model. Furthermore, the error values of SVM and linear regression were also higher than the proposed EDP scheme.\n                  <\/jats:p>","DOI":"10.3233\/jifs-191458","type":"journal-article","created":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T11:54:35Z","timestamp":1586519675000},"page":"4753-4765","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Energy demand forecasting of buildings using random neural networks"],"prefix":"10.1177","volume":"38","author":[{"given":"Jawad","family":"Ahmad","sequence":"first","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahsen","family":"Tahir","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadi","family":"Larijani","sequence":"additional","affiliation":[{"name":"School of Computing Engineering and Built Environment, Glasgow Caledonian University, Glasgow, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fawad","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, HITEC University Taxila, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed","family":"Aziz Shah","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam James","family":"Hall","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"William J.","family":"Buchanan","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2016.2597746"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2627403"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/en10101579"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2004.06.001"},{"issue":"08","key":"e_1_3_1_6_2","article-title":"An introduction to intelligent buildings: benefits and technology","volume":"13","author":"Holden J.","unstructured":"HoldenJ., An introduction to intelligent buildings: benefits and technology, Information Paper IP 13(08).","journal-title":"Information Paper IP"},{"key":"e_1_3_1_7_2","unstructured":"CarliniJ. The intelligent building definition handbook IBI Washington DC."},{"key":"e_1_3_1_8_2","unstructured":"HealeyG. Intelligent buildings: Integrated systems and controls (2011)."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1080\/17508975.2013.786874"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.01.108"},{"key":"e_1_3_1_11_2","unstructured":"AminP. CherkasovaL. AitkenR. and KacheV. Analysis and demand forecasting of residential energy consumption at multiple time scales in: 2019 IFIP\/IEEE Symposium on Integrated Network and Service Management (IM) IEEE 2019 pp. 494\u2013499."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2006.10.027"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.04.095"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2012.02.049"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2014.01.069"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2018.04.192"},{"key":"e_1_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Al KhafafN. JaliliM. and SokolowskiP. Application of deep learning long short-term memory in energy demand forecasting in: International Conference on Engineering Applications of Neural Networks Springer 2019 pp. 31\u201342.","DOI":"10.1007\/978-3-030-20257-6_3"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","unstructured":"HrnjicaB. and MehrA.D. Energy demand forecasting using deep learning in: Smart Cities Performability Cognition & Security Springer 2020 pp. 71\u2013104.","DOI":"10.1007\/978-3-030-14718-1_4"},{"key":"e_1_3_1_19_2","unstructured":"EseyeA.T. LehtonenM. TukiaT. UimonenS. and MillarR.J. Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems IEEE Access."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2015.01.047"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","unstructured":"WangZ. and SrinivasanR.S. A review of artificial intelligence based building energy prediction with a focus on ensemble prediction models in: Winter Simulation Conference (WSC) 2015 IEEE 2015 pp. 3438\u20133448.","DOI":"10.1109\/WSC.2015.7408504"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2015.11.037"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2016.10.079"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2008.11.035"},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","unstructured":"LiQ. RenP. and MengQ. Prediction model of annual energy consumption of residential buildings in: Advances in Energy Engineering (ICAEE) 2010 International Conference on IEEE 2010 pp. 223\u2013226.","DOI":"10.1109\/ICAEE.2010.5557576"},{"key":"e_1_3_1_26_2","unstructured":"PaudelS. NguyenP.H. KlingW.L. ElmitriM. LacarriereB. and CorreO.L. Support vector machine in prediction of building energy demand using pseudo dynamic approach arXiv preprint arXiv:1507.05019."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2015.09.097"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.502"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxp032"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1993.5.1.154"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-191458","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-191458","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-191458","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:00Z","timestamp":1777455660000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-191458"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,8]]},"references-count":29,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,4,30]]}},"alternative-id":["10.3233\/JIFS-191458"],"URL":"https:\/\/doi.org\/10.3233\/jifs-191458","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,8]]}}}