IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i2p409-d479449.html
   My bibliography  Save this article

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

Author

Listed:
  • Samer Chaaraoui

    (International Centre for Sustainable Development (IZNE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany)

  • Matthias Bebber

    (International Centre for Sustainable Development (IZNE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany)

  • Stefanie Meilinger

    (International Centre for Sustainable Development (IZNE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany)

  • Silvan Rummeny

    (Cologne Institute for Renewable Energy (CIRE), University of Applied Sciences Cologne, 50679 Cologne, Germany)

  • Thorsten Schneiders

    (Cologne Institute for Renewable Energy (CIRE), University of Applied Sciences Cologne, 50679 Cologne, Germany)

  • Windmanagda Sawadogo

    (Institute of Geography, University of Augsburg, 86159 Augsburg, Germany)

  • Harald Kunstmann

    (Institute of Geography, University of Augsburg, 86159 Augsburg, Germany
    Campus Alpin, Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), 82467 Garmisch-Partenkirchen, Germany)

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.

Suggested Citation

  • Samer Chaaraoui & Matthias Bebber & Stefanie Meilinger & Silvan Rummeny & Thorsten Schneiders & Windmanagda Sawadogo & Harald Kunstmann, 2021. "Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms," Energies, MDPI, vol. 14(2), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:409-:d:479449
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/2/409/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/2/409/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    2. Yang, YouLong & Che, JinXing & Li, YanYing & Zhao, YanJun & Zhu, SuLing, 2016. "An incremental electric load forecasting model based on support vector regression," Energy, Elsevier, vol. 113(C), pages 796-808.
    3. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    4. Changkyu Choi & Myung Hoon Yi, 2018. "The Internet, R&D expenditure and economic growth," Applied Economics Letters, Taylor & Francis Journals, vol. 25(4), pages 264-267, February.
    5. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    6. Paul Bertheau & Ayobami Solomon Oyewo & Catherina Cader & Christian Breyer & Philipp Blechinger, 2017. "Visualizing National Electrification Scenarios for Sub-Saharan African Countries," Energies, MDPI, vol. 10(11), pages 1-20, November.
    7. Nailya Maitanova & Jan-Simon Telle & Benedikt Hanke & Matthias Grottke & Thomas Schmidt & Karsten von Maydell & Carsten Agert, 2020. "A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports," Energies, MDPI, vol. 13(3), pages 1-23, February.
    8. Ömer Özgür Bozkurt & Göksel Biricik & Ziya Cihan Tayşi, 2017. "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.
    9. Sergio Bruno & Gabriella Dellino & Massimo La Scala & Carlo Meloni, 2019. "A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †," Energies, MDPI, vol. 12(6), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jieyun Zheng & Linyao Zhang & Jinpeng Chen & Guilian Wu & Shiyuan Ni & Zhijian Hu & Changhong Weng & Zhi Chen, 2021. "Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM," Energies, MDPI, vol. 14(8), pages 1-14, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
    2. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
    3. Donghun Lee & Seok Mann Yoon & Jaeseung Lee & Kwanho Kim & Sang Hwa Song, 2020. "Applying Deep Learning to the Heat Production Planning Problem in a District Heating System," Energies, MDPI, vol. 13(24), pages 1-17, December.
    4. Ren, Siyu & Hao, Yu & Xu, Lu & Wu, Haitao & Ba, Ning, 2021. "Digitalization and energy: How does internet development affect China's energy consumption?," Energy Economics, Elsevier, vol. 98(C).
    5. Oyewo, Ayobami Solomon & Solomon, A.A. & Bogdanov, Dmitrii & Aghahosseini, Arman & Mensah, Theophilus Nii Odai & Ram, Manish & Breyer, Christian, 2021. "Just transition towards defossilised energy systems for developing economies: A case study of Ethiopia," Renewable Energy, Elsevier, vol. 176(C), pages 346-365.
    6. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    7. Aunedi, Marko & Pantaleo, Antonio Marco & Kuriyan, Kamal & Strbac, Goran & Shah, Nilay, 2020. "Modelling of national and local interactions between heat and electricity networks in low-carbon energy systems," Applied Energy, Elsevier, vol. 276(C).
    8. Ramesh Chandra Das & Sujata Mukherjee, 2020. "Do Spending on R&D Influence Income? An Enquiry on the World’s Leading Economies and Groups," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 11(4), pages 1295-1315, December.
    9. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    10. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    11. Li, Menghan & Zhang, Kaiyue & Alamri, Ahmad Mohammed & Ageli, Mohammed Moosa & Khan, Numan, 2023. "Resource curse hypothesis and sustainable development: Evaluating the role of renewable energy and R&D," Resources Policy, Elsevier, vol. 81(C).
    12. Sima Siami‐Namini & Darren Hudson & Adao Alexandre Trindade & Conrad Lyford, 2019. "Commodity price volatility and U.S. monetary policy: Commodity price overshooting revisited," Agribusiness, John Wiley & Sons, Ltd., vol. 35(2), pages 200-218, April.
    13. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    14. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    15. Mst. Shapna Akter & Hossain Shahriar & Reaz Chowdhury & M. R. C. Mahdy, 2022. "Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach," Future Internet, MDPI, vol. 14(9), pages 1-23, August.
    16. Duarte Kazacos Winter & Rahul Khatri & Michael Schmidt, 2021. "Decentralized Prosumer-Centric P2P Electricity Market Coordination with Grid Security," Energies, MDPI, vol. 14(15), pages 1-17, August.
    17. Mugabe Roger & Liu Shulin & Brima Sesay, 2022. "ICT Development, Innovation Diffusion and Sustainable Growth in Sub-Saharan Africa," SAGE Open, , vol. 12(4), pages 21582440221, October.
    18. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    19. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
    20. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:409-:d:479449. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.