4.7 Article

Air compressor load forecasting using artificial neural network

Journal

EXPERT SYSTEMS WITH APPLICATIONS
Volume 168, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.114209

Keywords

Load forecasting; Air compressor; Artificial neural network; FFNN; LSTM

Funding

  1. United States Department of Energy [DE-EE0007711]

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The study used artificial neural networks to predict the electrical load of air compressors and validated two models under different control mechanisms. The results showed that both networks worked well for compressors using variable speed drive, while only the long short-term memory model provided acceptable results for compressors using on/off control. However, the models yielded unsatisfactory results for load/unload type air compressors.
Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network in air compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems' electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load management and look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Network and Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. The forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient of determination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and mean absolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield good results for compressors using variable speed drive (average R-2 = 0.8 and no naive forecasting), only the long short-term memory model gives acceptable results for compressors using on/off control (average R-2 = 0.82 and no naive forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting naive forecasting).

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