The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …

Energy consumption forecasting for buildings has immense value in energy efficiency and
sustainability research. Accurate energy forecasting models have numerous implications in …

We propose a combined method that is based on the fuzzy time series (FTS) and
convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in …

The electrical load forecast is getting more and more important in recent years due to the
electricity market deregulation and integration of renewable resources. To overcome the …

The paper studies a recently developed evolutionary-based image encryption algorithm. A
novel image encryption algorithm based on a hybrid model of deoxyribonucleic acid (DNA) …

Abstract Vehicle-to-grid (V2G) operation of plug-in electric vehicles (PEVs) is attracting
increasing attention since it can assist to improve the efficiency and reliability of power grid …

Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …

In recent years the use of renewable energy including wind energy has risen dramatically.
Because of the increasing development of wind power production, improvement of the …

In recent years, the demand for developing low computational cost methods to deal with
uncertainties in forecasting has been increased. Probabilistic forecasting is a class of …

This paper proposes a new electric load forecasting model by hybridizing the fuzzy time
series (FTS) and global harmony search algorithm (GHSA) with least squares support vector …