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Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards non-technical loss (NTL) analysis for electric utilities using genetic algorithm (GA) and support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector...
A hybrid genetic algorithm (HGA) is presented to solve the thermal unit commitment (UC). It effectively uses the binary-coded genetic algorithm incorporating a priority list ordering scheme to solve the unit commitment up/down problem and the lambda-iteration method to solve the power economic dispatch problem, This modification helps to explore the search space very effectively, It generates better...
Biclustering in microarray data is used to discover a set of genes expressed similarly in a subset of conditions. Biclustering algorithms require to identify coherent and non-trivial biclusters, i.e., the biclusters should have low mean squared residue and high row variance. This article presents a genetic algorithm based biclustering technique that optimizes a combination of these objectives. A novel...
Nurse scheduling problem (NSP) represents a subclass of constraint satisfaction problems (CSP), involving a set of constraints. The problem is highly constrained and difficult to solve. The goal is to find high quality shift assignments to nurses satisfying constraints related to labor contract rules, requirements of nurses as well as the employers in health-care institutions. The constraints are...
Distributed generation (DG) sources are becoming more prominent in distribution systems due to increased demand for the electrical energy. The locations and capacities of DG sources will have an impact on system losses, voltage profile characteristics of distribution network. This paper presents a heuristic approach for selection of optimal location and determination of optimal capacity of DG sources...
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