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.
Highlights
Load forecasting is a topic of great interest for electricity utilities because it enables them to make decisions on generation planning, the purchasing of electric power and future infrastructure development, and it is very important for demand response actions
Once the theoretical background is set and the different methods used for Short-Term LoadForecasting (STLF) have been introduced, the experimental results are presented
This paper presents the methodology used to select the best performing method for forecasting the electricity load demand in two existing distribution power networks of the OSIRIS Smart Grid demonstration project, focusing on the short-term forecast of aggregated data from two different power networks
Summary
Load forecasting is a topic of great interest for electricity utilities because it enables them to make decisions on generation planning, the purchasing of electric power and future infrastructure development, and it is very important for demand response actions. Load forecasting involves the accurate prediction of the electric demand in a geographical area within a planning horizon. Based on this horizon, load forecasting is usually classified into three categories [1]: Short-Term Load. Energies 2017, 10, 190 from the OSIRIS (Optimisation of Intelligent Monitoring of the Distribution Network) project: an experimental implementation of a smart grid demonstration project in the Madrid area managed by the Utility Gas Natural Fenosa. In this area, there are different types of networks (residential, commercial and industrial)
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