Recent advances in electricity price forecasting: A review of probabilistic forecasting
J Nowotarski, R Weron - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Since the inception of competitive power markets two decades ago, electricity price
forecasting (EPF) has gradually become a fundamental process for energy companies' …
forecasting (EPF) has gradually become a fundamental process for energy companies' …
[HTML][HTML] Electricity price forecasting: A review of the state-of-the-art with a look into the future
R Weron - International journal of forecasting, 2014 - Elsevier
A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the
last 15 years, with varying degrees of success. This review article aims to explain the …
last 15 years, with varying degrees of success. This review article aims to explain the …
Electricity price forecasting on the day-ahead market using machine learning
The price of electricity on the European market is very volatile. This is due both to its mode of
production by different sources, each with its own constraints (volume of production …
production by different sources, each with its own constraints (volume of production …
A robust optimization approach for optimal load dispatch of community energy hub
As an important segment in the multi-energy systems, energy hub plays a significant role in
improving the efficiency, flexibility and reliability of the multi-energy systems. In addition …
improving the efficiency, flexibility and reliability of the multi-energy systems. In addition …
Electricity price forecasting using recurrent neural networks
Accurate electricity price forecasting has become a substantial requirement since the
liberalization of the electricity markets. Due to the challenging nature of electricity prices …
liberalization of the electricity markets. Due to the challenging nature of electricity prices …
Neural network-based uncertainty quantification: A survey of methodologies and applications
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 …
optimization in many fields of science and engineering. The field has gained an …
Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization
This paper evaluates the real-time price-based demand response (DR) management for
residential appliances via stochastic optimization and robust optimization approaches. The …
residential appliances via stochastic optimization and robust optimization approaches. The …
Deep learning-based multivariate probabilistic forecasting for short-term scheduling in power markets
JF Toubeau, J Bottieau, F Vallée… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In the current competition framework governing the electricity sector, complex dependencies
exist between electrical and market data, which complicates the decision-making procedure …
exist between electrical and market data, which complicates the decision-making procedure …
Day-ahead electricity price forecasting via the application of artificial neural network based models
IP Panapakidis, AS Dagoumas - Applied Energy, 2016 - Elsevier
Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …
generation companies. However, the deregulation of electricity markets created a …
Comprehensive review of neural network-based prediction intervals and new advances
A Khosravi, S Nahavandi, D Creighton… - IEEE Transactions on …, 2011 - ieeexplore.ieee.org
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 …
of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian …