Stochastic Approximation (SA) is a classical algorithm that has had since the early days a
huge impact on signal processing, and nowadays on machine learning, due to the necessity …

A comprehensive 14-period study was conducted to model the energy system of a
residential area. The study aimed to compare actual energy consumption patterns with …

With the increasing uncertainty caused by the complexity of the world's energy environment
and the increasing penetration rate of renewable energy, it is significant to estimate the …

Tracking objects is a crucial problem in image processing and machine vision, involving the
representation of position changes of an object and following it in a sequence of video …

The use of machine learning techniques to forecast heating load, a crucial component of
building energy efficiency is examined in this work. Numerous building characteristics are …

In the new electricity market, the accurate electricity demand prediction can make high
possible profit. However, electricity consumption data exhibits nonlinearity, high volatility …

Nowadays, the growth of using a huge volume of data in various industries makes it crucial
to adopt efficient tools and methods to collect, process, and use them to gain a competitive …

This paper proposes a novel short-term forecasting model for day-ahead electricity market
demand and supply price/volume curves. These curves are intrinsically monotonic. The …

This paper employs the DeepAR algorithm to conduct short-term electrical load forecasting
specifically tailored for three distinct regions: commercial, residential, and industrial areas …