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Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms

  • Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.

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Metadaten
Document Type:Article
Language:English
Author:Samer Chaaraoui, Matthias Bebber, Stefanie Meilinger, Silvan Rummeny, Thorsten Schneiders, Windmanagda Sawadogo, Harald Kunstmann
Parent Title (English):energies
Volume:14
Issue:2
Pagenumber:22
First Page:409
ISSN:1996-1073
URN:urn:nbn:de:hbz:1044-opus-52958
DOI:https://doi.org/10.3390/en14020409
Publisher:MDPI
Place of publication:Basel
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2021/01/13
Note:
Funding: This research is part of the project EnerSHelF, which is funded by the German Federal Ministry of Education and Research as part of the CLIENT II program. Funding reference number: 03SF0567A-G.
Note:
Copyright: © 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Tag:Ghanaian health sector; LSTM; SARIMA; West Africa; load forecasting; neural network
Departments, institutes and facilities:Fachbereich Elektrotechnik, Maschinenbau, Technikjournalismus
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Internationales Zentrum für Nachhaltige Entwicklung (IZNE)
Dewey Decimal Classification (DDC):6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Open Access Funding:Hochschule Bonn-Rhein-Sieg / Graduierteninstitut
Entry in this database:2021/01/14