Machine and statistical learning algorithms can be reliably automated applied at scale. Therefore, they constitute a considerable asset for designing practical forecasting systems, such as those related to urban water demand. Quantile regression are machine that provide probabilistic forecasts in straightforward way, have not been so far demand forecasting. In this work, we fill gap, thereby pr...

The mathematical models of forecasting depend upon the managers conception regarding the actions development for long – term forecasting or for short – term forecasting. To this end, we distinguish two directions: an explorative one and a normative one. From the point of view of the investigating way, the methods and models of forecasting can be synthetic (aggregated) and morphologic, with a hi...

Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, minimizing trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, microgrids. Conventional load techniques obtain single-value forecasts by exploiting consumption patterns past demand. Howev...

A k-nearest neighbor (kNN) ensemble model has been developed to generate Probability Density Function (PDF) forecasts for intra-hour Direct Normal Irradiance (DNI). This probabilistic forecasting model, which uses diffuse irradiance measurements and cloud cover information as exogenous feature inputs, adaptively provides arbitrary PDF forecasts for different weather conditions. The proposed mod...