We present a novel approach to probabilistic electricity price forecasting which utilizes
distributional neural networks. The model structure is based on a deep neural network …

Accurate and reliable probabilistic wind power and wind speed forecasts provide large
amounts of uncertainty information, which is important for wind farm management and grid …

Deep learning has been widely applied to automated leakage detection and location of
natural gas pipe networks. Prevalent deep learning approaches do not consider the spatial …

Currently, wind power prediction has so many problems in the ultra-short-term time scale (0–
4h), which is difficult to improve the deterministic prediction and probability prediction …

Accurate forecasting of wind power faces two challenges: 1) extracting more effective
information on power fluctuations from limited input features, and 2) constructing a suitable …

To provide accurate renewable energy forecasts that adapt to the country's sustainable
development, a novel seasonal model combined with the data-restacking technique is …

Solar photovoltaic (PV) energy plays a vital role in global renewable energy generation.
Accurate and reliable solar energy forecasting is the key to improving energy scheduling …

The optimal combination of power distribution feeder reconfiguration (PDFR) with distributed
generators (DGs) is one of the most attractive combinatorial optimization issues. This paper …

The crude oil price has been subject to periodic fluctuations because of seasonal changes
in industrial demand and supply, weather, natural disasters and global political unrest. An …

Accurate river flow forecasting is of great importance for the scientific management of water
resources system. With the advantages of easy implementation and high flexibility, artificial …