The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies
of single models in processing various patterns and relationships latent in data, hybrid …

The high penetration of electric vehicles (EVs) will burden the existing power delivery
infrastructure if their charging and discharging are not adequately coordinated. Dynamic …

This paper uses artificial neural networks (ANNs) based ensemble machine learning for
improving short-term electricity load forecasting. Unlike existing methods, the proposed …

A novel, hybrid structure for week-ahead load forecasting is presented. It is the energy
market evolution that compels its participants to require load predictions whose accuracy …

In this article, a systematic literature review of 419 articles on energy demand modeling,
published between 2015 and 2020, is presented. This provides researchers with an …

Load forecasting is crucial for multiple energy management tasks such as scheduling
generation capacity, planning supply and demand, and minimizing energy trade costs. Such …

Forecasting future electricity consumption is one of the critical processes for addressing
energy management and supply–demand balance in modern electrical systems. In this …

This article presents a novel hybrid approach using classic statistics and machine learning
to forecast the national demand of electricity. As investment and operation of future energy …

The urge to increase renewable energy penetration into the power supply mix has been
frequently highlighted in response to climate change. South Korea was analyzed as a case …

In order to improve the prediction accuracy, this paper proposes a short‐term power load
forecasting method based on the improved exponential smoothing grey model. It firstly …