Improving Power Generation Efficiency using Deep Neural Networks

TL;DR

This paper demonstrates that deep neural networks can significantly improve load forecasting accuracy in power generation, aiding better capacity planning and dynamic pricing, despite increased computational demands.

Contribution

It introduces the application of deep neural networks for power load forecasting, showing their superiority over traditional methods in accuracy.

Findings

DNN methods outperform traditional load forecasting techniques.

Deep neural networks require more computational resources.

Results support dynamic pricing strategies.

Abstract

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.