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Comparison of Echo State Network with ANN-Based Forecasting Model for Solar Power Generation Forecasting

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Computational Intelligence in Machine Learning (ICCIML 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1106))

  • 84 Accesses

Abstract

Utilization of electricity from solar and wind has increased lately, due to which its integration with grid support and reliability is our priority. This can only be possible when we have accurate forecasting models, which have the potential to handle nonlinear data. Forecasting helps us to make decisions for coordination among devices and scheduling which ensures reliability. Solar power exhibits nonlinear characteristics because its output power is dependent on nature. Machine learning models can handle nonlinear data very efficiently. Hence, an echo state network-based (ESN) forecasting model is developed for forecasting solar power and made a comparison with the conventional ML model, i.e., the ANN model. To evaluate the model performance, a statistical method is used such as mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and correlation of determination (R2). The model performance is good if it shows the minimum value for MAE, MSE, and MAPE and the maximum value for R2, which is obtained for ESN-based forecasting model. Hence, we can conclude that the ESN performance is better than ANN-based prediction model. Forecasting is done on solar power generation to predict a day ahead, a week ahead, two weeks ahead, and a month ahead.

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Authors and Affiliations

  1. National Institute of Technology, Raipur, India

    Shashikant, Binod Shaw & Jyoti Ranjan Nayak

  2. OP Jindal University, Raigarh, India

    Shashikant

Authors
  1. Shashikant
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  2. Binod Shaw
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  3. Jyoti Ranjan Nayak
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Correspondence to Shashikant .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India

    Vinit Kumar Gunjan

  2. BioAxis DNA Research Centre, Hyderabad, India

    Amit Kumar

  3. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Sri Niwas Singh

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Shashikant, Shaw, B., Nayak, J.R. (2024). Comparison of Echo State Network with ANN-Based Forecasting Model for Solar Power Generation Forecasting. In: Gunjan, V.K., Kumar, A., Zurada, J.M., Singh, S.N. (eds) Computational Intelligence in Machine Learning. ICCIML 2022. Lecture Notes in Electrical Engineering, vol 1106. Springer, Singapore. https://doi.org/10.1007/978-981-99-7954-7_13

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