The real-world building can be regarded as a comprehensive energy engineering system;
its actual energy consumption depends on complex affecting factors, including various …

Load forecasting is an integral part of the power industries. Load-forecasting techniques
should minimize the percentage error while prediction future demand. This will inherently …

The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting
the energy industry into a modern era of reliable and sustainable energy networks. This …

This paper proposes an effective deep learning framework for Short-Term Load Forecasting
(STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional …

With advances in science and technology, the demand for electricity is increasing
dramatically. Consequently, reliable short-term power load prediction is critical to ensure the …

This paper addresses the estimation of household communities' overall energy usage and
solar energy production, considering different prediction horizons. Forecasting the electricity …

Electricity consumption forecasting plays an important role in investment planning of
electricity infrastructure, and in electricity production/generation and distribution. Accurate …

In this article, a fuzzy logic model is proposed for more precise hourly electrical power
demand modeling in New England. The issue that exists when considering hourly electrical …

This paper presents the development of a global and integrated sizing approach under
different performance indexes applied to fuel cell/battery hybrid power systems. The strong …

Household power load forecasting plays an important role in the operation and planning of
power grids. To address the prediction issue of household power consumption in power …