Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project Articles uri icon

publication date

  • February 2017

issue

  • 2

volume

  • 10

Electronic International Standard Serial Number (EISSN)

  • 1996-1073

abstract

  • This paper presents the implementation of an adaptive load forecasting methodology in two different power networks from a smart grid demonstration project deployed in the region of Madrid, Spain. The paper contains an exhaustive comparative study of different short-term load forecast methodologies, addressing the methods and variables that are more relevant to be applied for the smart grid deployment. The evaluation followed in this paper suggests that the performance of the different methods depends on the conditions of the site in which the smart grid is implemented. It is shown that some non-linear methods, such as support vector machine with a radial basis function kernel and extremely randomized forest offer good performance using only 24 lagged load hourly values, which could be useful when the amount of data available is limited due to communication problems in the smart grid monitoring system. However, it has to be highlighted that, in general, the behavior of different short-term load forecast methodologies is not stable when they are applied to different power networks and that when there is a considerable variability throughout the whole testing period, some methods offer good performance in some situations, but they fail in others. In this paper, an adaptive load forecasting methodology is proposed to address this issue improving the forecasting performance through iterative optimization: in each specific situation, the best short-term load forecast methodology is chosen, resulting in minimum prediction errors.

subjects

  • Industrial Engineering

keywords

  • short-term load forecasting; smart grids; machine-to-machine (m2m) communications; time series; distribution networks; neural-networks; system; requirements; models; infrastructures; challenges