Wind Energy Forecasting at Different Time Horizons with Individual and Global Models

This paper has been presented at: 14th IFIP International Conference on Artificial Intelligence Applications and InnovationsIn this work two different machine learning approaches have been studied to predict wind power for different time horizons: individual and global models. The individual approach constructs a model for each horizon while the global approach obtains a single model that can be used for all horizons. Both approaches have advantages and disadvantages. Each individual model is trained with data pertaining to a single horizon, thus it can be specific for that horizon, but can use fewer data for training than the global model, which is constructed with data belonging to all horizons. Support Vector Machines have been used for constructing the individual and global models. This study has been tested on energy production data obtained from the Sotavento wind farm and meteorological data from the European Centre for Medium-Range Weather Forecasts, for a 5 × 5 grid around Sotavento. Also, given the large amount of variables involved, a feature selection algorithm (Sequential Forward Selection) has been used in order to improve the performance of the models. Experimental results show that the global model is more accurate than the individual ones, specially when feature selection is used.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R

A Comparison of Decoding Strategies for the 0/1 Multi-objective Unit Commitment Problem

International audienceIn the single objective Unit Commitment Problem (UCP) the problem is usually separated in two sub-problems : the commitment problem which aims to fix the on/off scheduling of each unit and the dispatching problem which goal is to schedule the production of each turned on unit. The dispatching problem is a continuous convex prob-lem that can easily be solved exactly. For the first sub-problem genetic algorithms (GA) are often applied and usually handle binary vectors rep-resenting the solutions of the commitment problem.Then the solutions are decoded in solving the dispatching problem with an exact method to obtain the precise production of each unit. In this paper a multi-objective version of the UCP taking the emission of gas into account is presented. In this multi objective UCP the dispatching problem re-mains easy to solve whereas considering it separatly remains interesting. A multi-objective GA handling binary vectors is applied. However for a binary representation there is a set of solutions of the dispatching prob-lem that are pareto equivalent. Three decoding strategies are proposed and compared. The main contribution of this paper is the third decoding strategy which attaches an approximation of the Pareto front from the associated dispatching problem to each genotypic solution. It is shown that this decoding strategy leads to better results in comparison to the other ones