Abstract
Many episode-based (or direct) policy search algorithms, maintain a multivariate Gaussian distribution as search distribution over the parameter space of some objective function. One class of algorithms, such as episodic REPS, PoWER or PI2 uses, a weighted maximum likelihood estimate (WMLE) to update the mean and covariance matrix of this distribution in each iteration. However, due to high dimensionality of covariance matrices and limited number of samples, the WMLE is an unreliable estimator. The use of WMLE leads to overfitted covariance estimates, and, hence the variance/entropy of the search distribution decreases too quickly, which may cause premature convergence. In order to alleviate this problem, the estimated covariance matrix can be regularized in different ways, for example by using a convex combination of the diagonal covariance estimate and the sample covariance estimate. In this paper, we propose a new covariance matrix regularization technique for policy search methods that uses the convex combination of the sample covariance matrix and the old covariance matrix used in last iteration. The combination weighting is determined by specifying the desired entropy of the new search distribution. With this mechanism, the entropy of the search distribution can be gradually decreased without damage from the maximum likelihood estimate.

Abstract
In this paper, islanding detection in a hybrid distributed generation (DG) system is analyzed by the use of hyperbolic S-transform (HST), time-time transform, and mathematical morphology methods. The merits of these methods are thoroughly compared against commonly adopted wavelet transform (WT) and S-transform (ST) techniques, as a new contribution to earlier studies. The hybrid DG system consists of photovoltaic and wind energy systems connected to the grid within the IEEE 30-bus system. Negative sequence component of the voltage signal is extracted at the point of common coupling and passed through the above-mentioned techniques. The efficacy of the proposed methods is also compared by an energy-based technique with proper threshold selection to accurately detect the islanding phenomena. Further, to augment the accuracy of the result, the classification is done using support vector machine (SVM) to distinguish islanding from other power quality (PQ) disturbances. The results demonstrate effective performance and feasibility of the proposed techniques for islanding detection under both noise-free and noisy environments, and also in the presence of harmonics.

Abstract
A recent solution to tackle environmental issues is the electrification of transportation. Effective integration of plug-in electric vehicles (PEVs) into the grid is important in the process of achieving sustainable development. One of the key solutions regarding the need for charging stations is the installation of PEV parking lots (PLs). However, contrary to common parkings, PLs are constrained by various organizations such as municipalities, urban traffic regulators, and electrical distribution systems. Therefore, this paper aims to allocate PLs in distribution systems with the objective of minimizing system costs including power loss, network reliability, and voltage deviation as possible objectives. A two-stage model has been designed for this purpose. PLs' behavior considering market interactions is optimized at the first stage to provide profit to the PL owner. At the second stage, the PL allocation problem is solved considering various network constraints. Conclusions are duly drawn with a realistic example.

Abstract
Stochastic search algorithms are general black-box optimizers. Due to their ease of use and their generality, they have recently also gained a lot of attention in operations research, machine learning and policy search. Yet, these algorithms require a lot of evaluations of the objective, scale poorly with the problem dimension, are affected by highly noisy objective functions and may converge prematurely. To alleviate these problems, we introduce a new surrogate-based stochastic search approach. We learn simple, quadratic surrogate models of the objective function. As the quality of such a quadratic approximation is limited, we do not greedily exploit the learned models. The algorithm can be misled by an inaccurate optimum introduced by the surrogate. Instead, we use information theoretic constraints to bound the 'distance' between the new and old data distribution while maximizing the objective function. Additionally the new method is able to sustain the exploration of the search distribution to avoid premature convergence. We compare our method with state of art black-box optimization methods on standard uni-modal and multi-modal optimization functions, on simulated planar robot tasks and a complex robot ball throwing task. The proposed method considerably outperforms the existing approaches.

Abstract
Humans tend to organize their knowledge into hierarchies, because searches are efficient when proceeding downward in the tree-like structures. Similarly, many autonomous robots also contain some form of hierarchical knowledge. They may learn knowledge from their experiences through interaction with human users. However, it is difficult to find a common ground between robots and humans in a low level experience. Thus, their interaction must take place at the semantic level rather than at the perceptual level, and robots need to organize perceptual experiences into hierarchies for themselves. This paper presents an unsupervised method to build view-based perceptual hierarchies using hierarchical Nearest Neighbor Graphs (hNNGs), which combine most of the interesting features of both Nearest Neighbor Graphs (NNGs) and self-balancing trees. An incremental construction algorithm is developed to build and maintain the perceptual hierarchies. The paper describes the details of the data representations and the algorithms of hNNGs.

Abstract
Electricity industries throughout the world have been using load profiles for many years. Electrical load data contain valuable information that can be useful for both electricity producers and consumers. Load forecasting is a fundamental and important task to operate power systems efficiently and economically. Currently, prediction intervals (PIs) are assuming increasing importance comparatively to point forecast that cannot properly handle forecast uncertainties, since they are capable to compromise informativeness and correctness. This paper aims to demonstrate that different demand profiles clearly influence PIs reliability and width. The evaluation is performed using data from different customers on the basis of their electricity behavior using hierarchical clustering, and taking the Kullback-Leibler divergence as the distance metric. PIs are obtained using two different strategies: (1) dual perturb and combine algorithm and (2) conformal prediction. It was possible to demonstrate that different demand profiles clearly influence PI reliability and width for both models. The knowledge retrieved from the analysis of the load patterns is useful and can be used to support the selection of the best method to interval forecast, considering a specific location. And also, it can support the selection of an optimum confidence level, considering that a too wide PI conveys little information and is of no use for decision making.