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Although over a thousand scientific papers address the topic of load forecasting every year, only a few are dedicated to finding a general framework for load forecasting that improves the performance, without depending on the unique characteristics of a certain task such as geographical location. Meta-learning, a powerful approach for algorithm selection has so far been demonstrated only on uni...

Purpose – The purpose of this paper is to develop an integrated model for manpower forecasting for the information security (IS) industry, one of the fastest growing IT-related industries. The proposed model incorporates three critical factors (feedback structure, time lags, and a flexible saturation point) in a system dynamics (SD) simulation frame. Design/methodology/approach – A simulation m...

Increased levels of electrification of home appliances, heating and transportation are bringing new challenges for the smart grid, as energy supply sources need to be managed more efficiently. In order to minimize production costs, reduce the impact on the environment, and optimize electricity pricing, producers need to be able to accurately estimate their customers’ demand. As a result, foreca...

Wind speed forecasting can accurately improve prediction efficiency of wind power in wind farm, decrease failure probability of wind turbine, and extend life cycle. An innovative algorithm is proposed to optimize both the parameters of least squares support vector machine (LSSVM) and the procedure of finding sparse support vector. Firstly, the defects of support vector are analyzed. Then inequa...

Hydro-thermal scheduling is the problem of finding an optimal dispatch of power plants in a system containing both hydro and thermal plants. Since hydro plants are able to store water over long time periods, and since future inflows are uncertain due to precipitation, the resulting multi-stage stochastic optimization problem becomes challenging to solve. Several solution methods have been devel...

Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing server shutdowns because of overheating and improving a data center’s energy efficiency. This paper presents a novel cyber-physical approach for temperature forecasting in data centers, which integrates Computational Fluid Dynamics (CFD)...

In recent years, many studies have been done on forecasting fuzzy time series. First-order fuzzy time series forecasting methods with first-order lagged variables and high-order fuzzy time series forecasting methods with consecutive lagged variables constitute the considerable part of these studies. However, these methods are not effective in forecasting fuzzy time series which contain seasonal...