Abstract tAccurate forecasting simultaneously becomes more important and more
challenging due to the increasing penetration of photovoltaic (PV) systems in the built …

Uncertainty quantification plays a critical role in the process of decision making and
optimization in many fields of science and engineering. The field has gained an …

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

Due to the intermittency and uncertainty in photovoltaic (PV) power outputs, not only
deterministic point predictions (DPPs), but also associated prediction Intervals (PIs) are …

Energy management systems are designed to monitor, optimize, and control the smart grid
energy market. Demand-side management, considered as an essential part of the energy …

Electrical power systems are evolving from today's centralized bulk systems to more
decentralized systems. Penetrations of renewable energies, such as wind and solar power …

In the current competition framework governing the electricity sector, complex dependencies
exist between electrical and market data, which complicates the decision-making procedure …

This paper evaluates the four leading techniques proposed in the literature for construction
of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian …

Prediction intervals (PIs) have been proposed in the literature to provide more information by
quantifying the level of uncertainty associated to the point forecasts. Traditional methods for …

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and
optimization of power systems. However, the volatility and intermittence of wind power pose …