{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:40:53Z","timestamp":1772768453545,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council (EPSRC)","doi-asserted-by":"publisher","award":["EP\/T517896\/1"],"award-info":[{"award-number":["EP\/T517896\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relatively easier. Nevertheless, in order to ensure high-accuracy forecasting, it is crucial to consider suitable algorithms and the impact of the extracted features. This paper presents a framework to evaluate a total of nine forecasting algorithms categorised into single and multistage models, constructed from the Prophet, Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and the Least Absolute Shrinkage and Selection Operator (LASSO) approaches, applied to an electricity demand dataset from an NHS hospital. The aim is to see such techniques widely used in accurately predicting energy consumption, limiting the negative impacts of future waste on energy, and making a contribution towards the 2050 net zero carbon target. The proposed method accounts for patterns in demand and temperature to accurately forecast consumption. The Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to evaluate the algorithms\u2019 performance. The results show the superiority of the Long Short-Term Memory (LSTM) model and the multistage Facebook Prophet model, with R2 values of 87.20% and 68.06%, respectively.<\/jats:p>","DOI":"10.3390\/fi15040134","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T08:27:27Z","timestamp":1680251247000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Comparative Study of Single and Multi-Stage Forecasting Algorithms for the Prediction of Electricity Consumption Using a UK-National Health Service (NHS) Hospital Dataset"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1246-8981","authenticated-orcid":false,"given":"Ahmad","family":"Taha","sequence":"first","affiliation":[{"name":"James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9126-7613","authenticated-orcid":false,"given":"Basel","family":"Barakat","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Sunderland, Sunderland SR6 0DD, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2024-7313","authenticated-orcid":false,"given":"Mohammad M. A.","family":"Taha","sequence":"additional","affiliation":[{"name":"Independent Researcher, Dover, NH 03820, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3393-8460","authenticated-orcid":false,"given":"Mahmoud A.","family":"Shawky","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4169-4438","authenticated-orcid":false,"given":"Chun Sing","family":"Lai","sequence":"additional","affiliation":[{"name":"Brunel Interdisciplinary Power Systems Research Centre, Brunel University London, London UB8 3PH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1802-9728","authenticated-orcid":false,"given":"Sajjad","family":"Hussain","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8411-1041","authenticated-orcid":false,"given":"Muhammad Zainul","family":"Abideen","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7097-9969","authenticated-orcid":false,"given":"Qammer H.","family":"Abbasi","sequence":"additional","affiliation":[{"name":"James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","unstructured":"NHS (2020). Delivering a \u2019Net Zero\u2019 National Health Service, National Health Service, NHS England and NHS Improvement. Technical Report."},{"key":"ref_2","unstructured":"NHS (2015). NHS Energy Efficiency Fund-Final Report, National Health Service. Technical Report."},{"key":"ref_3","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":"Suganthi","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1109\/TPWRS.1987.4335210","article-title":"The Time Series Approach to Short Term Load Forecasting","volume":"2","author":"Hagan","year":"1987","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/59.317646","article-title":"A real-time implementation of short-term load forecasting for distribution power systems","volume":"9","author":"Fan","year":"1994","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.procs.2015.04.160","article-title":"A Review of Short Term Load Forecasting using Artificial Neural Network Models","volume":"48","author":"Baliyan","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/59.932287","article-title":"Short-term hourly load forecasting using time-series modeling with peak load estimation capability","volume":"16","author":"Amjady","year":"2001","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/S0360-5442(97)00032-7","article-title":"Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis","volume":"22","year":"1997","journal-title":"Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1109\/TPAS.1984.318745","article-title":"Load Forecasting for Trnasmission Planning","volume":"PAS-103","author":"Willis","year":"1984","journal-title":"IEEE Trans. Power Appar. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at scale","volume":"72","author":"Taylor","year":"2018","journal-title":"Am. Stat."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","article-title":"Short-term residential load forecasting based on LSTM recurrent neural network","volume":"10","author":"Kong","year":"2017","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., and Namin, A.S. (2018, January 17\u201320). A comparison of ARIMA and LSTM in forecasting time series. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00227"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.procs.2017.12.087","article-title":"Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters","volume":"125","author":"Kumar","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.rser.2019.04.002","article-title":"Hybrid machine intelligent SVR variants for wind forecasting and ramp events","volume":"108","author":"Dhiman","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.apenergy.2017.03.034","article-title":"Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings","volume":"195","author":"Chen","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_16","first-page":"64","article-title":"A short-term load forecasting method using artificial neural networks and wavelet analysis","volume":"1","author":"Ekonomou","year":"2016","journal-title":"Int. J. Power Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Karampelas, P., Vita, V., Pavlatos, C., Mladenov, V., and Ekonomou, L. (2010, January 23\u201325). Design of artificial neural network models for the prediction of the Hellenic energy consumption. Proceedings of the 10th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2010), Belgrade, Serbia.","DOI":"10.1109\/NEUREL.2010.5644049"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Stratigakos, A., Bachoumis, A., Vita, V., and Zafiropoulos, E. (2021). Short-term net load forecasting with singular spectrum analysis and LSTM neural networks. Energies, 14.","DOI":"10.3390\/en14144107"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"512","DOI":"10.3390\/ai2040032","article-title":"Hybrid Machine Learning Models for Forecasting Surgical Case Volumes at a Hospital","volume":"2","author":"Aravazhi","year":"2021","journal-title":"AI"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.enbuild.2015.05.056","article-title":"Electrical consumption forecasting in hospital facilities: An application case","volume":"103","author":"Bagnasco","year":"2015","journal-title":"Energy Build."},{"key":"ref_21","unstructured":"Tsakoumis, A.C., Vladov, S.S., and Mladenov, V.M. (2002, January 26\u201328). Electric load forecasting with multilayer perceptron and Elman neural network. Proceedings of the 6th Seminar on Neural Network Applications in Electrical Engineering, Belgrade, Yugoslavia."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Barakat, B., Taha, A., Samson, R., Steponenaite, A., Ansari, S., Langdon, P.M., Wassell, I.J., Abbasi, Q.H., Imran, M.A., and Keates, S. (2021). 6G Opportunities Arising from Internet of Things Use Cases: A Review Paper. Future Internet, 13.","DOI":"10.3390\/fi13060159"},{"key":"ref_23","unstructured":"(2023, February 09). EnergyLogix. EnergyLogix Software, Helping to Manage Energy Consumption Information. Available online: https:\/\/energylogix.com\/energy-monitoring\/software\/."},{"key":"ref_24","unstructured":"(2023, February 09). Science and Knowledge Service. JRC Photovoltaic Geographical Information System (PVGIS)-European Commission. Available online: https:\/\/europa.eu\/capacity4dev\/afretep\/wiki\/pvgis-photovoltaic-geographical-information-system."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1109\/JIOT.2018.2877510","article-title":"LASSO and LSTM Integrated Temporal Model for Short-Term Solar Intensity Forecasting","volume":"6","author":"Wang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)?\u2013Arguments against avoiding RMSE in the literature","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. 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