The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.

Given the intractability of domain-independent planning, the ability to control the search of a planner is vitally important. One way of doing this involves learning from search failures. This paper describes SNLP+EBL, the first implementation of explanation based search control rule learning framework for a partial order (plan-space) planner. We will start by describing the basic learning framework of SNLP+EBL. We will then concentrate on SNLP+EBL's ability to learn from failures, and describe the results of empirical studies which demonstrate the effectiveness of the search-control rules SNLP+EBL learns using our method.We then demonstrate the generality of our learning methodology by extending it to UCPOP [39], a descendant of SNLP that allows for more expressive domain theories. The resulting system, UCPOP+EBL, is used to analyze and understand the factors influencing the effectiveness of EBL. Specifically, we analyze the effect of (i) expressive action representations (ii) domain specific failure theories and (iii) sophisticated backtracking strategies on the utility of EBL. Through empirical studies, we demonstrate that expressive action representations allow for more explicit domain representations which in turn increase the ability of EBL to learn from analytical failures, and obviate the need for domain specific failure theories. We also explore the strong affinity between dependency directed backtracking and EBL in planning.

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