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

Chaaraoui, Samer; Bebber, Matthias; Meilinger, Stefanie; Rummeny, Silvan; Schneiders, Thorsten; Sawadogo, Windmanagda; Kunstmann, Harald

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. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000131312
Veröffentlicht am 11.04.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1996-1073
KITopen-ID: 1000131312
HGF-Programm 12.11.33 (POF IV, LK 01) Regional Climate and Hydrology
Erschienen in Energies
Verlag MDPI
Band 14
Heft 2
Seiten Art. Nr.: 409
Vorab online veröffentlicht am 13.01.2021
Schlagwörter West Africa; Ghanaian health sector; load forecasting; LSTM; neural network; SARIMA
Nachgewiesen in Web of Science
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