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A useful tool for the efficient management of the electric power grid is the accurate, ahead-of-time prediction-of-load demand. A novel methodology for very-short-term load forecasting is introduced in this paper, and its performance is tested on a set of historical, demand-side, 5-min data. The approach employs an ensemble of kernel-based Gaussian processes (GPs) whose predictions constitute the terms of a linear model. Adoption of a set of cost functions assessing model accuracy allows the formulation of a multiobjective optimization problem with respect to model coefficients. A genetic algorithm (GA) is used to search for a solution based on the previous step data while Pareto optimality theory provides the necessary conditions to identify an optimal one. Thus, it is the optimized linear model that yields the final prediction over the designated time interval. The proposed methodology is examined on 5-min-interval predictions for 30-min-ahead horizon. It is compared with support vector regression (SVR) and autoregressive moving average (ARMA) models as well as the independent GP forecasters on a set of six cost functions. Results clearly promote the proposed forecasting method not only over individual GPs but also over SVR and ARMA.Index Terms-Gaussian process (GP) ensemble, nondominated sorting genetic algorithm-II (NSGA-II), Pareto optimal, very-short-term load forecasting (VSTLF).

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of API, which are suitable for raising the abstraction level and hiding complexity. It implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.

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