Combinatorics

Electrical engineering

Physics

Meteorology

Probability density function

Wind power forecasting

Mathematics

Kernel (algebra)

Statistics

Mathematical optimization

Probabilistic forecasting

Engineering

Estimator

Artificial intelligence

Wind speed

Power (physics)

Geography

Computer science

Probabilistic logic

Quantum mechanics

Kernel density estimation

Wind power

Electric power system

Environmental science

Artificial neural network

IEEE Transactions on Power Systems

Unsteady motion of the atmosphere incurs nonlinear and spatiotemporally coupled uncertainties in the wind power prediction (WPP) of multiple wind farms. This brings both opportunities and challenges to wind power probabilistic forecasting (WPPF) of a wind farm cluster or region, particularly when wind power is highly penetrated within the power system. This paper proposes an Improved Deep Mixture Density Network (IDMDN) for short-term WPPF of multiple wind farms and the entire region. In this respect, a deep multi-to-multi (m2m) mapping Neural Network model, which adopts the beta kernel as the mixture component to avoid the density leakage problem, is established to produce probabilistic forecasts in an end-to-end manner. A novel modified activation function and several general training procedures are then introduced to overcome the unstable behavior and NaN (Not a Number) loss issues of the beta kernel function. Verification of IDMDN is based on an open-source dataset collected from seven wind farms, and comparison results show that the proposed model improves the WPPF performance at both wind farm and regional levels. Furthermore, a laconic and accurate probabilistic expression of predicted power at each time step is produced by the proposed model.

Improved Deep Mixture Density Network for Regional Wind Power Probabilistic Forecasting