Prediction interval

Deep learning

Combinatorics

Electrical engineering

Physics

Meteorology

Wind power forecasting

Mathematics

Algorithm

Geodesy

SCADA

Boom

Machine learning

Engineering

Residual

Benchmark (surveying)

Artificial intelligence

Wind speed

Power (physics)

Geography

Grid

Renewable energy

Reliability engineering

Geometry

Interval (graph theory)

Computer science

Quantum mechanics

Wind power

Electric power system

Environmental science

Environmental engineering

Frontiers in Energy Research

Wind is a pollution-free renewable energy source. It has attracted increasing attention owing to the decarbonization of electricity generation. However, owing to the dynamic nature of wind speed, ensuring a stable supply of wind energy to electric grid networks is challenging. Therefore, accurate short-term forecasting of wind power prediction plays a key role for wind farm engineers. With the boom in AI technologies, deep-learning-based forecasting models have demonstrated superior performance in wind power forecasting. This paper proposes a short-term deep-learning-based interval prediction algorithm for forecasting short-term wind power generation in wind farms. The proposed approach combines the lower upper bound estimation (LUBE) method and a deep residual network (DRN). Wind farm data collected in northwestern China are selected for this empirical study. The proposed approach is compared with three benchmark short-term forecasting approaches. Extensive experiments conducted on the data collected from five wind turbines in 2021 indicate that the proposed algorithm is efficient, stable, and reliable.

SCADA Data Based Wind Power Interval Prediction Using LUBE-Based Deep Residual Networks