Abstract
The study is based on time series modeling with special application to electric load consumptions modeling in a distributed environment. Existing theories and applications about artificial neural networks, backpropagation learning method, Nguyen-Widrow weight initialization technique and autocorrelation analysis were explored and applied. An adaptive stopping criterion algorithm was also integrated to BP to enable the ANN to converge on the global minimum and stop the training process without human intervention. These algorithms were combined together in designing the parallel adaptive multi-layer perceptron (PAMLP). In the simulation, the electric load consumptions of the seven (7) power utilities from Alaska in 1990–2013 were obtained from the official website of U.S. Energy Information Administration. The data set was divided into three overlapping parts: training, testing and validation sets, based on the principles of sliding-window training and walk-forward testing methods. The PAMLPs were trained and tested using the sliding-window method with 15-year window size and walk-forward testing method, respectively. The accuracy of each forecasting model produced by PAMLP was then measured using the respective out-of-sample validation sets using RMSD, CV (RMSD), and SMAPE (0% ≤ SMAPE ≤ 100%). In the monthly basis time series forecasting, the average CV (RMSD) at 7.79% and SMAPE at 3.12% for all utilities show the effectiveness of the PAMLP system across different time horizons and origin.
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Acknowledgements
This work (Grants No. C0515862) was supported by Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration in 2017.
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Lalis, J.T., Gerardo, B.D., Byun, Y. (2019). A Mechanism for Online and Dynamic Forecasting of Monthly Electric Load Consumption Using Parallel Adaptive Multilayer Perceptron (PAMLP). In: Lee, R. (eds) Computer and Information Science. ICIS 2018. Studies in Computational Intelligence, vol 791. Springer, Cham. https://doi.org/10.1007/978-3-319-98693-7_12
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