While the field of electricity price forecasting has benefited from plenty of contributions in the
last two decades, it arguably lacks a rigorous approach to evaluating new predictive …

Artificial intelligence techniques lead to data-driven energy services in distribution power
systems by extracting value from the data generated by the deployed metering and sensing …

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 …

Abstract The Electricity Supply Chain is a system of enabling procedures to optimize
processes ranging from production to transportation and consumption of electricity. The …

The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM)
directly affect the its temperature rise. The accurate evaluation and prediction of stator …

Deep neural networks are rapidly gaining popularity. However, their application requires
setting multiple hyper-parameters, and the performance relies strongly on this choice. We …

The convolutional neural network (CNN) is commonly used in visual recognitions and
classifications. However, CNN can also be applied as a forecaster that can extract features …

Despite the extensive research on electricity price forecasting, forecasting imbalance prices
is a relatively new topic. Interest, however, is growing because of the greater uncertainties …

Day-ahead prediction of electricity market price is a key for market participants to make a
bidding strategy. While numerous methods for day-ahead market price (DMP) forecasting …

Liberalized electricity markets have been studied for the past few decades with different
mathematical techniques. Operating these markets under the growing uncertainties has …