Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

Mathematical Problems in Engineering, Jun 2013

This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme.

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Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids Alma Y. Alanis,1 Luis J. Ricalde,2 Chiara Simetti,3 and Francesca Odone3 1CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Colonia Las Aguilas, 45080 Zapopan, JAL, Mexico 2UADY, Faculty of Engineering, Avenida Industrias no Contaminantes por Periferico Norte, Apartado Postal 115 Cordemex, Merida, Yuc, Mexico 3DISI, Università degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy Received 29 March 2013; Accepted 27 May 2013 Academic Editor: Yudong Zhang Copyright © 2013 Alma Y. Alanis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. 1. Introduction The limited existing reserves of fossil fuels and the harmful emissions associated with them have led to an increased focus on renewable energy applications in recent years. The first steps on integrating renewable energy sources began with hybrid wind and solar systems as complementing sources and as a solution for rural applications and weak grid interconnections. Further research has implemented hybrid systems including several small scale renewable energy sources as solar thermal, biomass, fuel cells, and tidal power. Since the production costs for photovoltaic and wind turbine applications have considerably reduced, they have become the primary choice for hybrid energy generation systems. The future of energy production is headed towards this scheme of integration of renewable energy sources with existing conventional generation systems with a high degree of measurement, communications, and control. This integration is defined as a smart grid. This new scheme increases the power quality since the production becomes decentralized and is the main reason for which institutions have increased the research on this concept [1]. Microgrids integrate small scale energy generation systems mainly from renewable energy and implement complex control technologies to improve the flexibility and reliability of the power system. The design of these systems integrates a distributed power generation system and a management unit composed of a communication network which monitor and controls the interconnection between energy sources, storage devices, and electrical loads. Among renewable energy sources, wind energy is the one with the lowest cost of electricity production [2]. However, in practice the integration of wind energy into the existing electricity supply system is a real challenge because its availability mainly depends on meteorological conditions, particularly on the magnitude of the wind speed, which cannot directly be changed by human intervention. For this reason, it is important to have a reliable estimation of wind velocity and direction which directly affects the energy generation. Integration of the forecast of wind speed and output power is a good way to improve the performance in scheduling for smart grids [3]. Wind prediction is not an easy task; the wind has a stochastic nature with high rate of change. Wind speed time series present highly nonlinear behavior with no typical patterns and a weak seasonal character [4]. Several methods have been proposed to accomplish wind characteristics forecasting like numerical weather prediction systems, statistical approaches, and artificial neural networks using feedforward or recurrent structures [2, 5–9]. In [6], a linear-time-series-based model relating the predicted interval to its corresponding one and data covering a temporal span of two years is developed. The statistical approaches have the advantage of low cost since they only require historical data; on the other hand, the accuracy of the prediction drops for long time horizons. Artificial intelligence methods are more suitable for short-term predictions; these methods are based on time series historical data in order to build a mathematical model which approximates the input-output relationship. Time-series-based models include the autoregressive (AR) and the autoregr (...truncated)


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Alma Y. Alanis, Luis J. Ricalde, Chiara Simetti, Francesca Odone. Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids, Mathematical Problems in Engineering, 2013, 2013, DOI: 10.1155/2013/197690