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Appl. Sci. 2018, 8(5), 749; https://doi.org/10.3390/app8050749

Climate Change and Power Security: Power Load Prediction for Rural Electrical Microgrids Using Long Short Term Memory and Artificial Neural Networks

1
Department of Computer Science and Engineering, University of Alaska Anchorage, Anchorage, AK 99508, USA
2
Department of Mechanical Engineering, University of Alaska Anchorage, Anchorage, AK 99508, USA
*
Author to whom correspondence should be addressed.
Received: 7 February 2018 / Revised: 16 April 2018 / Accepted: 26 April 2018 / Published: 9 May 2018
(This article belongs to the Special Issue Applications of Artificial Neural Networks for Energy Systems)
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Abstract

Many of rural Alaskan communities operate their own, stand-alone electrical microgrids as there is no state-wide power distribution network. Although the fossil fuel-based power generators are the primary energy source in these isolated communities, an increasing number of microgrids have started to diversify their power supply by including renewable wind, hydro and photovoltaic energy sources. The integration and optimization of the multiple energy sources requires a design of a new power management system that can anticipate how much electricity will be drawn by the community and how much electricity will be generated from the renewable sources in order to control the fossil fuel-based generators to meet the community power demand. To address this problem, we designed a hybrid machine learning algorithm to predict community power draw as one module for the next generation microgrid power management system. The algorithm divides the task of a power load prediction into two sub-models: the first temporal model predicts the future weather conditions and the second model is trained to associate the predicted weather conditions with the community power demand. The results illustrate (1) the feasibility of building a machine learning algorithm that uses relatively small data for model training and validation, (2) the ability to predict the near-future community power load for the microgrids operating in the environments with highly dynamic weather conditions and (3) how the integration of multiple low quality future weather conditions produced high accuracy power load prediction. View Full-Text
Keywords: power load prediction; long short term memory; neural networks; power management systems power load prediction; long short term memory; neural networks; power management systems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Cenek, M.; Haro, R.; Sayers, B.; Peng, J. Climate Change and Power Security: Power Load Prediction for Rural Electrical Microgrids Using Long Short Term Memory and Artificial Neural Networks. Appl. Sci. 2018, 8, 749.

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