Citation: Taha, A.; Barakat, B.; Taha, M.M.A.; Shawky, M.A.; Lai, C.S.; Hussain, S.; Abideen, M.Z.; Abbasi, Q.H. 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. Future Internet 2023, 15, 134. https://doi.org/10.3390/ fi15040134 Academic Editor: Ivan Miguel Pires Received: 10 February 2023 Revised: 25 March 2023 Accepted: 27 March 2023 Published: 31 March 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). future internet Article 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 Ahmad Taha 1, * ,† , Basel Barakat 2, * ,† , Mohammad M. A. Taha 3 , Mahmoud A. Shawky 1,† , Chun Sing Lai 4 , Sajjad Hussain 1 , Muhammad Zainul Abideen 1 and Qammer H. Abbasi 1 1 James Watt School of Engineering, College of Science and Engineering, University of Glasgow, Glasgow G12 8QQ, UK 2 School of Computer Science, University of Sunderland, Sunderland SR6 0DD, UK 3 Independent Researcher, Dover, NH 03820, USA 4 Brunel Interdisciplinary Power Systems Research Centre, Brunel University London, London UB8 3PH, UK * Correspondence: ahmad.taha@glasgow.ac.uk (A.T.); basel.barakat@sunderland.ac.uk (B.B.) † These authors contributed equally to this work. Abstract: 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 (R 2 ), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to evaluate the algorithms’ performance. The results show the superiority of the Long Short-Term Memory (LSTM) model and the multistage Facebook Prophet model, with R 2 values of 87.20% and 68.06%, respectively. Keywords: artificial intelligence; energy forecasting; energy management; electrical demand forecasting; hospital; National Health Service; net zero carbon target 1. Introduction Recently, the National Health Service (NHS) of the United Kingdom (UK) outlined plans to achieve net zero emissions by 2050, with an 80% reduction by 2028–2032 [1]. The NHS generates 18% of all emissions deriving from the UK’s non-domestic buildings [2]. This is mainly due to the high energy consumption of hospitals. For the sake of reducing energy consumption, hospitals can adopt precise forecasting techniques to predict energy usage and facilitate the development of effective solutions to overcome potential increases. In this context, multiple forecasting techniques have been proposed in recent years, and they can be categorised into, but not limited to, time series models, regression models, econometric models, genetic algorithm models, and others [3]. Time series models are simple, as they use the time-series trends from time-stamped historical data to predict the future energy demand. This work implements and utilises time series models that predict future energy consumption based on previously captured time-stamped data. Load forecasting of electricity demand has been widely investigated, with studies considering various sectors and geographical locations, and it is categorised into short-, Future Internet 2023, 15, 134. https://doi.org/10.3390/fi15040134 https://www.mdpi.com/journal/futureinternet