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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 energysolutions. Photovoltaic-hybrid systems become more and more important for rural electrificationdue to their potential to offer a clean and cost-effective energy supply. However, uncertainties relatedto the prediction of electrical loads and solar irradiance result in inefficient system control and canlead to an unstable electricity supply, which is vital for the high reliability required for applicationswithin the health sector. Model predictive control (MPC) algorithms present a viable option to tacklethose 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 aGhanaian health facility, considering initially limited knowledge of load and pattern changes throughthe implementation of incremental learning. The correlation of the electrical load with exogenousvariables was determined to map out possible enhancements within the algorithms. Results showthat 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 SARIMAalgorithm and the linear regression model show extreme error outliers of nRMSE >1, methods viathe LSTM model and the customized statistical approaches perform better with a median nRMSE of0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study isa favoring towards the LSTM model and the statistical approach, with regard to MPC applicationswithin photovoltaic-hybrid system solutions in the Ghanaian health sector.

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Metadaten
Verfasserangaben:Samer Chaaraoui, Matthias Bebber, Stefanie Meilinger, Silvan Rummeny, Thorsten Schneiders, Windmanagda Sawadogo, Harald Kunstmann
URN:urn:nbn:de:hbz:832-epub4-16547
DOI:https://doi.org/10.3390/en14020409
ISSN:1996-1073
Titel des übergeordneten Werkes (Englisch):Energies
Jahr der Fertigstellung:2020
Verlag:MDPI
Dokumentart:Wissenschaftlicher Artikel
Sprache:Englisch
Datum des Hochladens:06.05.2021
GND-Schlagwort:Elektrizitätsversorgung; Ghana; Medizinische Einrichtung; Westafrika
Freies Schlagwort / Tag:Ghanaian health sector; LSTM; Load forecasting; Neural network; SARIMA; West Africa
Jahrgang:14
Ausgabe / Heft:2
Seitenzahl:22
Fakultäten und Zentrale Einrichtungen:Informations-, Medien- und Elektrotechnik (F07) / Fakultät 07 / Institut für Elektrische Energietechnik
DDC-Sachgruppen:500 Naturwissenschaften und Mathematik
Open Access:Open Access
DeepGreen:DeepGreen
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International