Prediction of building energy consumption plays an important role in energy conservation,
management, and planning. Continuously improving and enhancing the performance of …

We consider image transformation problems, where an input image is transformed into an
output image. Recent methods for such problems typically train feed-forward convolutional …

This paper describes a single-image super-resolution (SR) algorithm based on nonnegative
neighbor embedding. It belongs to the family of single-image example-based SR algorithms …

Accurate and high-resolution data reflecting different climate scenarios are vital for policy
makers when deciding on the development of future energy resources, electrical …

Single-image super-resolution is of great importance for vision applications, and numerous
algorithms have been proposed in recent years. Despite the demonstrated success, these …

This paper proposes a framework for single-image super-resolution. The underlying idea is
to learn a map from input low-resolution images to target high-resolution images based on …

Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both
electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed …

Machine learning (ML) systems can automatically mine data sets for hidden features or
relationships. Recently, ML methods have become increasingly used within many scientific …

The goal of single-image super-resolution is to generate a high-quality high-resolution
image based on a given low-resolution input. It is an ill-posed problem which requires …

Node localisation is one of the significant concerns in Wireless Sensor Networks (WSNs). It
is a process in which we estimate the coordinates of the unknown nodes using sensors with …