a b s t r a c tIn this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, M exico, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5e23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models.
Abstract:The problem of solving large Markov decision processes accurately and quickly is challenging. Since the computational effort incurred is considerable, current research focuses on finding superior acceleration techniques. For instance, the convergence properties of current solution methods depend, to a great extent, on the order of backup operations. On one hand, algorithms such as topological sorting are able to find good orderings but their overhead is usually high. On the other hand, shortest path methods, such as Dijkstra's algorithm which is based on priority queues, have been applied successfully to the solution of deterministic shortest-path Markov decision processes. Here, we propose an improved value iteration algorithm based on Dijkstra's algorithm for solving shortest path Markov decision processes. The experimental results on a stochastic shortest-path problem show the feasibility of our approach.
Existe una tendencia mundial por el uso de energías limpias en sustitución de combustibles fósiles, dado el constante incremento de la demanda energética y el interés por preservar el medio ambiente. De las energías renovables en el ámbito mundial, la energía eólica es la que ha tenido más crecimiento en los últimos años. Sin embargo, en el caso de México se tienen ciertas dificultades aún para extender su uso en ciertas regiones del territorio nacional. Una dificultad es la de conocer con suficiente anterioridad cuánta energía se dispondrá para inyectar a la red eléctrica. El presente trabajo describe el desarrollo de una tecnología de Inteligencia Artificial (IA) para el pronóstico de potencia eólica basado en información meteorológica de varios años. Específicamente, se realizó una investigación detallada sobre el uso potencial de las Redes Bayesianas para estas aplicaciones de pronóstico. Se identificó la necesidad de considerar el tiempo en el proceso de pronóstico y se realizó una propuesta novedosa basada en Redes Bayesianas Dinámicas (RBD). El sistema de pronóstico se probó con datos meteorológicos del centro regional de tecnología eólica (CERTE) del Instituto Nacional de Electricidad y Energías Limpias (INEEL) en Oaxaca, México. Se compararon los resultados con predicciones hechas con series de tiempo y se encontraron resultados satisfactorios confirmando que las Redes Bayesianas Dinámicas son una herramienta prometedora para la predicción de la potencia eólica. Descriptores: Energía eólica, pronóstico de potencia, Inteligencia Artificial, Redes Bayesianas Dinámicas.
& In this paper we tackle the sailing strategies problem, a stochastic shortest-path Markov decision process. The problem of solving large Markov decision processes accurately and quickly is challenging. Because the computational effort incurred is considerable, current research focuses on finding superior acceleration techniques. For instance, the convergence properties of current solution methods depend, to a great extent, on the order of backup operations. On one hand, algorithms such as topological sorting are able to find good orderings, but their overhead is usually high. On the other hand, shortest path methods, such as Dijkstra's algorithm, which is based on priority queues, have been applied successfully to the solution of deterministic shortest-path Markov decision processes. Here, we propose improved value iteration algorithms based on Dijkstra's algorithm for solving shortest path Markov decision processes. The experimental results on a stochastic shortest-path problem show the feasibility of our approach.
In this paper, we present a new approach for the estimation of Markov decision processes based on efficient association rule mining techniques such as Apriori. For the fastest solution of the resulting association‐rule based Markov decision process, several accelerating procedures such as asynchronous updates and prioritization using a static ordering have been applied. A new criterion for state reordering in decreasing order of maximum reward is also compared with a modified topological reordering algorithm. Experimental results obtained on a finite state and action‐space stochastic shortest path problem demonstrate the feasibility of the new approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.