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Summary: Dispatching energy in transmission and distribution networks and bidding on electricity markets require probabilistic wind power forecasts available several hours before the actual occurrence. The volatility of the wind over large time horizons complicates the generation of skilled, reliable wind power forecasts. Exploiting numeric weather predictions (NWPs) is generally considered mandatory to increase the skill of probabilistic predictions, and forecasts may further be enhanced by adding several spatially distributed predictions. However, feature selection becomes a more complicated and time consuming as the number of NWPs increases. In this paper, we predict the power generated by a wind farm developing a new technique on the basis of ranking and combining spatially distributed NWPs, easing the feature selection and reducing the computational efforts, as well as maintaining high the skill of probabilistic forecasts. Several spatially distributed NWPs, provided for the area surrounding the wind farm, are ranked for each individual generator, and the ranked NWPs are combined to form an ensemble set of predictors for the probabilistic forecasting model. This ensemble is obtained using three different weighted combination approaches. Gradient boosting regression tree models and quantile regression neural networks generate probabilistic wind power forecasts. The proposed methodology is applied for day‐ahead wind power forecasting of individual generators and of the entire wind farm. Numerical experiments carried out on an actual wind farm in southern Italy suggest that ranking NWPs may keep the skill of forecasts at high levels even under a simplified, less computationally intensive procedure. The performance is also enhanced up to 1.8% with respect to standard techniques. [ABSTRACT FROM AUTHOR]

Short-term probabilistic load forecasting is essential to power systems management and optimization of power flows across transmission networks. Developing forecasting tools capable of providing accurate predictions must comply with their practical implementation in short-term operations, mainly in terms of fast computing and high efficiency. Many data-driven forecasting solutions are often unnecessarily verbose, thus making their practical value limited. This problem occurs more frequently when multiple loads have to be predicted simultaneously, as in power transmission system analysis and optimization. In this paper, we propose a new cooperative forecasting system that refines probabilistic forecasts of individual loads online. The refining procedure is based on a multivariate quantile regression, which is dynamically applied to the individual forecasts as new observations become available. The proposal is validated on the load data published by ISO New England for eight regions, covering six states of the United States. The quality of probabilistic forecasts is assessed in terms of reliability and sharpness, comparing the results to three benchmarks. The proposed method outperforms the best benchmark by up to 6% w.r.t. the reduction in pinball loss. [ABSTRACT FROM AUTHOR]

• A multivariate approach to probabilistic industrial load forecasting is proposed. • Univariate quantile regression and quantile regression forests generate individual forecasts. • A multivariate quantile regression model combines individual forecasts of active and reactive power. • Multivariate quantile regression on quantile regression forests is the best pick for aggregate load. • Multivariate quantile regression on univariate quantile regression is the best pick for a single load. Industrial load takes a big portion of the total electricity demand. Skilled probabilistic industrial load forecasts allow for optimally exploiting energy resources, managing the reserves, and market bidding, which are beneficial to transmission and distribution system operators and their industrial customers. Despite its importance, industrial load forecasting has never been a popular subject in the literature. Most existing methods operate on the active power alone, partially or totally neglecting the reactive power. This paper proposes a multivariate approach to probabilistic industrial load forecasting, which addresses active and reactive power simultaneously. The proposed method is based on a two-level procedure, which consists of generating probabilistic forecasts individually for active and reactive power through univariate probabilistic models, and combining these forecasts in a multivariate approach based on a multivariate quantile regression model. The procedure to estimate the parameters of the multivariate quantile regression model is posed in this paper under a linear programming problem, to facilitate the convergence to the optimal solution. The proposed method is validated using actual load data collected at an Italian factory, under comparison with several probabilistic benchmarks. The proposed multivariate method enhances the skill of forecasts by 6% to 13.5%, with respect to univariate benchmarks. [ABSTRACT FROM AUTHOR]