This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction a...

Forecasting has often played predominant roles in daily life as necessary measures can be taken to bypass the undesired and detrimental future prompted by this fact, the issue of forecasting becomes one of the most important topics of research for themodern scientists and as a result several innovative forecasting techniques have been developed. Amongst various well-known forecasting techniques...

IaaS providers have become interested in optimising their infrastructure energy efficiency. To do so, their VMplacement algorithmsneed to know the current and future energy efficiency at different levels (Virtual Machine, node, infrastructure and service levels) and for potential actions such as service deployment or VM deployment, migration or cancellation. This publication provides a mathemat...

The small medium large system (SMLsystem) is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH) for participation in the Solar Decathlon 2013 competition. Several technologies have been integrated to reduce power consumption. One of these is a forecasting system based on artificial neural networks (ANNs), which is able to predict indoor temperature in the near future using captured...

In every field of life, people are interested to be able to forecast future. A number of techniques are available to predict and forecasting upto a certain level of accuracy. Many techniques involve statistical tools and techniques for forecasting, modeling and control. Use of statistical techniques is growing with time and new techniques are being developed very rapidly. Especially in the fiel...

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the are...

The interest of researchers in different fields of science towards modern soft computing data driven methods for time series forecasting has grown in recent years. Modeling and forecasting hydrometeorological variables is an important step in understanding climate change. The application of modern methods instead of traditional statistical techniques has lead to great improvement in past studie...

Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart from a case study on hierarchical forecasting, t...

The massive development of the cloud marketplace is leading to an increase in the number of the Data Centers (DCs) globally and eventually to an increase of the CO2 related footprint. The calculation of the impact of Virtual Machines (VMs) on the environment is a challenging task, not only due to the technical difficulties but also due to the lack of information from the energy providers. The e...

High-quality products are considered as one of the most important practices for achieving success. However, it is really hard to predict and forecast the product quality due to some undetermined parameters. In order to forecast product quality from various aspects, we propose an integrated model of utilizing grey theory and neural network. In this paper, the grey forecasting model for product q...