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Article

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

1
International Centre for Sustainable Development (IZNE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
2
Cologne Institute for Renewable Energy (CIRE), University of Applied Sciences Cologne, 50679 Cologne, Germany
3
Institute of Geography, University of Augsburg, 86159 Augsburg, Germany
4
Campus Alpin, Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), 82467 Garmisch-Partenkirchen, Germany
*
Author to whom correspondence should be addressed.
Energies 2021, 14(2), 409; https://doi.org/10.3390/en14020409
Received: 30 November 2020 / Revised: 7 January 2021 / Accepted: 9 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue Artificial Intelligence Technologies for Electric Power Systems)
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. View Full-Text
Keywords: West Africa; Ghanaian health sector; load forecasting; LSTM; neural network; SARIMA West Africa; Ghanaian health sector; load forecasting; LSTM; neural network; SARIMA
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MDPI and ACS Style

Chaaraoui, S.; Bebber, M.; Meilinger, S.; Rummeny, S.; Schneiders, T.; Sawadogo, W.; Kunstmann, H. Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms. Energies 2021, 14, 409. https://doi.org/10.3390/en14020409

AMA Style

Chaaraoui S, Bebber M, Meilinger S, Rummeny S, Schneiders T, Sawadogo W, Kunstmann H. Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms. Energies. 2021; 14(2):409. https://doi.org/10.3390/en14020409

Chicago/Turabian Style

Chaaraoui, Samer, Matthias Bebber, Stefanie Meilinger, Silvan Rummeny, Thorsten Schneiders, Windmanagda Sawadogo, and Harald Kunstmann. 2021. "Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms" Energies 14, no. 2: 409. https://doi.org/10.3390/en14020409

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