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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

Taha, Ahmad, Barakat, Basel, Taha, Mohammad M. A., Shawky, Mahmoud A., Lai, Chun Sing, Hussain, Sajjad, Abideen, Muhammad Zainul and Abbasi, Qammer H. (2023) 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, 15 (4). p. 134. ISSN 1999-5903

Item Type: Article

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

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More Information

Uncontrolled Keywords: artificial intelligence; energy forecasting; energy management; electrical demand forecasting; hospital; National Health Service; net zero carbon target
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Depositing User: Basel Barakat

Identifiers

Item ID: 15890
Identification Number: https://doi.org/10.3390/fi15040134
ISSN: 1999-5903
URI: http://sure.sunderland.ac.uk/id/eprint/15890
Official URL: http://dx.doi.org/10.3390/fi15040134

Users with ORCIDS

ORCID for Basel Barakat: ORCID iD orcid.org/0000-0001-9126-7613

Catalogue record

Date Deposited: 03 Apr 2023 13:07
Last Modified: 17 Apr 2023 12:00

Contributors

Author: Basel Barakat ORCID iD
Author: Ahmad Taha
Author: Mohammad M. A. Taha
Author: Mahmoud A. Shawky
Author: Chun Sing Lai
Author: Sajjad Hussain
Author: Muhammad Zainul Abideen
Author: Qammer H. Abbasi

University Divisions

Faculty of Technology > School of Computer Science

Subjects

Computing > Data Science
Computing > Artificial Intelligence
Engineering > Electrical Engineering
Computing
Engineering

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