Accurate wind power forecasting plays a critical role in the operation of parks and dispatch energy into grid. With excellent automatic pattern recognition nonlinear mapping ability for big data, deep learning is increasingly employed forecasting. However, salient realities are that in-situ measured data relatively expensive inaccessible correlation between steps omitted most multistep forecast...

Load forecasting constitutes an important tool for efficient planning and operation of power systems and its significance has been intensifying particularly, because of the recent movement towards open energy markets and the need to assure high standards on reliability. Accurate load forecasting is of great importance for power system operation. It is the basis of economic dispatch, hydrotherma...

The prediction of near surface wind speed is becoming increasingly vital for the operation of electrical energy grids as the capacity of installed wind power grows. The majority of predictive wind speed modeling has focused on point-based time-series forecasting. Effectively balancing demand and supply in the presence of distributed wind turbine electricity generation, however, requires the pre...

The prediction of wind power output is part the basic work grid dispatching and energy distribution. At present, mainly obtained by fitting regressing historical data. medium- long-term results exhibit large deviations due to uncertainty generation. In order meet demand for accessing large-scale into electricity further improve accuracy short-term prediction, it necessary develop models accurat...

In this article, we propose a distributed demand-side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The grid model comprehends traditional users as well active (prosumers). Through rolling-horizon approach, prosumers participate DSM program, aiming at minimizing their cost the presence of uncertain generation by game theory approach. We ass...

The paper focuses on the forecasting models of green energy power systems and loads. Since electric energy cannot be stored efficiently in large quantities, accurate tracking of the energy generation in conjunction with the consumer requirements can lead to the optimization of energy costs in the context of the liberalization of the electric energy market and the increased usage of the alternat...

Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed t...