In this paper, a comprehensive review is presented for mid-term load forecasting. The basic
loads and effective factors are studied, and then several classifications are presented for …

Accurate forecasting of solar photovoltaic (PV) power for the next day plays an important role
in unit commitment, economic dispatch, and storage system management. However …

Unsupervised learning is a type of machine learning that learns from data without human
supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a …

In recent years, the field of data analytics has witnessed a surge in innovative techniques to
handle the ever-increasing volume and complexity of data. Among these, nature-inspired …

As smart meters have proliferated in recent years, electrical power companies are dealing
with a large volume of data, known as Big Data. Consistent with this issue, data science …

This paper introduces a novel coupling method to enhance the precision of short-and
medium-term renewable energy power load demand forecasting. Firstly, the Tent chaotic …

Renewable energies are being introduced in countries around the world to move away from
the environmental impacts from fossil fuels. In the residential sector, smart buildings that …

Electrical load forecasting (ELF) is gaining importance especially due to the severe impact of
climate change on electrical energy usage and dynamically evolving smart grid …

This research presents a comprehensive case study on medium-term load forecasting
(MTLF) in the intricate dynamics of Pakistan's power sector, Gujranwala Electric Power …

The rapid increase of residential heat pumps (HPs) being installed has a major impact on
the required capacity of distribution networks. To avoid congestion of assets, an efficient …