The accurate forecasting of the hourly month-ahead electricity consumption represents a
very important aspect for non-household electricity consumers and system operators, and at …

Load forecasting in smart grids is still exploratory; despite the increase of smart grids
technologies and energy conservation research, many challenges remain for accurate load …

The construction of smart grids has greatly changed the power grid pattern and power
supply structure. For the power system, reasonable power planning and demand response …

We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for
predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter …

Driven by technological advances, there is a increase in electricity-based equipments and
this leads to excessive energy consumption (EC) and demand for power every day. To …

This paper proposes a new method for short-term electric load forecasting using a Ridgelet
Neural Network (RNN) combined with a wavelet transform and optimized by a Self-Adapted …

This study forecasts electricity demand in a smart grid environment. We present a prediction
method that uses a combination of forecasting values based on time-series clustering. The …

Forecasting energy demand is critical to ensure the steady operation of the power system.
However, present approaches to estimating power load are still unsatisfactory in terms of …

Machine Learning (ML) and Deep Neural Network (DNN) based Computer-aided decision
(CAD) systems show the effective implementation in solving skin cancer classification …

Forecasting of building energy consumption plays a key role in the energy management of
the modern power system. However, the noise and randomness in the electricity load data …