Volltext-Downloads (blau) und Frontdoor-Views (grau)

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.

Download full text files

Export metadata

Additional Services

Search Google Scholar Check availability


Show usage statistics
Document Type:Article
Author:Samer Chaaraoui, Matthias Bebber, Stefanie Meilinger, Silvan Rummeny, Thorsten Schneiders, Windmanagda Sawadogo, Harald Kunstmann
Parent Title (English):energies
Article Number:409
Number of pages:22
Place of publication:Basel
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2021/01/13
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.
Funding Information: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.
Keyword:Ghanaian health sector; LSTM; SARIMA; West Africa; load forecasting; neural network
Departments, institutes and facilities:Fachbereich Ingenieurwissenschaften und Kommunikation
Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)
Internationales Zentrum für Nachhaltige Entwicklung (IZNE)
Projects:CLIENT II - Verbundvorhaben EnerSHelf: Energieversorgung für Gesundheitseinrichtungen in Ghana; Teilvorhaben Entwicklung und Analyse technischer Lösungen im länderspezifischen politisch-ökonomischen Kontext (DE/BMBF/03SF0567A)
Dewey Decimal Classification (DDC):3 Sozialwissenschaften / 33 Wirtschaft / 333.7 Natürliche Ressourcen, Energie und Umwelt
Open access funding:Hochschule Bonn-Rhein-Sieg / Graduierteninstitut
Entry in this database:2021/01/14
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International