A regional pretraining-classification-selection forecasting system for wind power point forecasting and interval forecasting

Prediction of Air Pollution Interval Based on Data Preprocessing and Multi-Objective Dragonfly Optimization Algorithm

With the rapid development of global industrialization and urbanization, as well as the continuous expansion of the population, large amounts of industrial exhaust gases and automobile exhaust are released. To better sound an early warning of air pollution, researchers have proposed many pollution prediction methods. However, the traditional point prediction methods cannot effectively analyze the volatility and uncertainty of pollution. To fill this gap, we propose a combined prediction system based on fuzzy granulation, multi-objective dragonfly optimization algorithm and probability interval, which can effectively analyze the volatility and uncertainty of pollution. Experimental results show that the combined prediction system can not only effectively predict the changing trend of pollution data and analyze local characteristics but also provide strong technical support for the early warning of air pollution.

Review on Deep Learning Research and Applications in Wind and Wave Energy

Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.