Short term power load prediction with knowledge transfer |
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| Affiliation: | 1. Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran;2. Department of ECE, University of Sultan Qaboos, Oman;1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, China;2. School of Computer Science, Carnegie Mellon University, United States;1. School of Aeronautics & Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China;2. Department of Electrical and Computer Engineering, Technische Universität München, Arcisstr. 21, 80333 Munich, Germany;3. Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, 48128, USA |
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| Abstract: | A novel transfer learning method is proposed in this paper to solve the power load forecast problems in the smart grid. Prediction errors of the target tasks can be greatly reduced by utilizing the knowledge transferred from the source tasks. In this work, a source task selection algorithm is developed and the transfer learning model based on Gaussian process is constructed. Negative knowledge transfers are avoided compared with the previous works, and therefore the prediction accuracies are greatly improved. In addition, a fast inference algorithm is developed to accelerate the prediction steps. The results of the experiments with real world data are illustrated. |
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| Keywords: | Transfer learning Gaussian process Power load prediction |
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