Improving Power Generation Efficiency using Deep Neural Networks
This addresses energy loss reduction for power systems, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of improving power generation efficiency by using deep neural networks (DNNs) for load forecasting from smart meter data, resulting in DNN methods outperforming most traditional methods.
Recently there has been significant research on power generation, distribution and transmission efficiency especially in the case of renewable resources. The main objective is reduction of energy losses and this requires improvements on data acquisition and analysis. In this paper we address these concerns by using consumers' electrical smart meter readings to estimate network loading and this information can then be used for better capacity planning. We compare Deep Neural Network (DNN) methods with traditional methods for load forecasting. Our results indicate that DNN methods outperform most traditional methods. This comes at the cost of additional computational complexity but this can be addressed with the use of cloud resources. We also illustrate how these results can be used to better support dynamic pricing.