Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Kernel regression based short-term load forecasting
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Hierarchically structured energy markets as novel smart grid control approach
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
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Electric grids are moving from a centralized single supply chain towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and dynamic scenario Soon, the growing smart meter infrastructure will allow the collection of terabytes of detailed data about the grid condition, e.g., the state of renewable electric energy producers or the power consumption of millions of private customers, in very short time steps For reliable prediction strong and fast regression methods are necessary that are able to cope with these challenges In this paper we introduce a novel regression technique, i.e., evolutionary local kernel regression, a kernel regression variant based on local Nadaraya-Watson estimators with independent bandwidths distributed in data space The model is regularized with the CMA-ES, a stochastic non-convex optimization method We experimentally analyze the load forecast behavior on real power consumption data The proposed method is easily parallelizable, and therefore well appropriate for large-scale scenarios in smart grids.