The balance between supplied and demanded power is a crucial issue in the economic
dispatching of electricity energy. With the emergence of renewable sources and data-driven …

The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …

This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …

This review emphasizes the critical need for accurate integration of solar energy into power
grids. It meticulously examines the advancements in transformer models for solar …

The aim of this paper is to develop machine learning based framework to short-term load
forecasting with high accuracy for residential building. The purpose is to develop a …

The power grid stability is highly impacted by the fluctuating nature of renewable energy
sources. This paper proposes a deep learning method-based bidirectional gated recurrent …

This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN)
models in time series analysis, specifically focusing on their application in prediction tasks of …

Cloud computing has emerged as a cornerstone for modern industries, offering scalable and
flexible resources to meet growing computational demands. However, managing fluctuating …

Abstract Adopting Internet of Things (IoT)-based technologies in smart homes helps users
analyze home appliances electricity consumption for better overall cost monitoring. The IoT …

Paper aims This study analyzed the feasibility of the BiGRU-CNN artificial neural network as
a forecasting tool for short-term electric load. This forecasting model can serve as a support …