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Performance Analysis of Artificial Neural Networks Training Algorithms and Activation Functions in Day-Ahead Base, Intermediate, and Peak Load Forecasting

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Book cover Advances in Information and Communication (FICC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 70))

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Abstract

Artificial Neural Networks (ANN) has been highly utilized in short term electric load forecasting not just among aggregated consumed load but also in predicting the specified base, intermediate and peak loads. To ensure success in its predictive capability, every ANN model implementation should employ the appropriate training algorithm and activation function that will be suitable to the historical data that it is processing. This study conducted performance analysis of six models having different combination of training algorithms namely Quick Propagation, Resilient Algorithm and Back Propagation and activation functions namely Gaussian and Sigmoid. Electric load data preparation was conducted through data correction, Min-Max data normalization and clustering to identify the base, intermediate and peak loads. After determining the ANN models’ input, hidden and output neurons from its respective layers, the ANN model having the combination of Quick Propagation training algorithm and Gaussian activation function yielded the lowest MSE and MAPE values having 0.005700397 and 5.88% respectively. The day-ahead base, intermediate, and peak load forecasting model developed in this study has the potential to be implemented in order to suffice the need of electric power systems in predicting the necessary system loads for their economic decisions, power dispatching, system planning, and reliability evaluation.

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Authors and Affiliations

  1. Premier Research Institute of Science and Mathematics, Mindanao State University-Iligan Institute of Technology, Iligan City, The Philippines

    Lemuel Clark P. Velasco, Noel R. Estoperez, Renbert Jay R. Jayson, Caezar Johnlery T. Sabijon & Verlyn C. Sayles

Authors
  1. Lemuel Clark P. Velasco
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  2. Noel R. Estoperez
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  3. Renbert Jay R. Jayson
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  4. Caezar Johnlery T. Sabijon
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  5. Verlyn C. Sayles
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Corresponding author

Correspondence to Lemuel Clark P. Velasco .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Velasco, L.C.P., Estoperez, N.R., Jayson, R.J.R., Sabijon, C.J.T., Sayles, V.C. (2020). Performance Analysis of Artificial Neural Networks Training Algorithms and Activation Functions in Day-Ahead Base, Intermediate, and Peak Load Forecasting. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_23

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