Universität zu Köln

Chaaraoui, Samer, Bebber, Matthias, Meilinger, Stefanie, Rummeny, Silvan, Schneiders, Thorsten, Sawadogo, Windmanagda and Kunstmann, Harald ORCID: 0000-0001-9573-1743 (2021). Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms. Energies, 14 (2). BASEL: MDPI. ISSN 1996-1073

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Abstract

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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Chaaraoui, Samer UNSPECIFIED UNSPECIFIED UNSPECIFIED Bebber, Matthias UNSPECIFIED UNSPECIFIED UNSPECIFIED Meilinger, Stefanie UNSPECIFIED UNSPECIFIED UNSPECIFIED Rummeny, Silvan UNSPECIFIED UNSPECIFIED UNSPECIFIED Schneiders, Thorsten UNSPECIFIED UNSPECIFIED UNSPECIFIED Sawadogo, Windmanagda UNSPECIFIED UNSPECIFIED UNSPECIFIED Kunstmann, Harald UNSPECIFIED orcid.org/0000-0001-9573-1743 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-589099
DOI:	10.3390/en14020409
Journal or Publication Title:	Energies
Volume:	14
Number:	2
Date:	2021
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	1996-1073
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Energy & Fuels Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/58909