In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …

The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …

Reliable and accurate zonal electricity load forecasting is essential for power system
operation and planning. Probabilistic load forecasts can present more comprehensive …

Privacy protection as a major concern of the industrial big data enabling entities makes the
massive safety-critical operation data of a wind turbine unable to exert its great value …

We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural
networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression …

Decarbonization of electricity systems drives significant and continued investments in
distributed energy sources to support the cost-effective transition to low-carbon energy …

Quantifying uncertainties in the prediction of building energy consumption is critical to
building energy management systems. In this study, a deep-learning-based interval …

The growth of distributed renewable energy and demand-side responsiveness has
increased the difficulty of mid-term hourly load time-series forecasting. This study presents a …

Wind power forecast is playing a significant role in the operation and dispatch of modern
power systems. Compared with traditional point forecast methods, interval forecast is able to …

This paper proposes a direct model for conditional probability density forecasting of
residential loads, based on a deep mixture network. Probabilistic residential load forecasting …