the aim of this paper is to develop a prediction model of energy demand of iran’s industrial sector. for that matter a markov chain grey model (mcgm) has been proposed to forecast such energy demand. to find the effectiveness of the proposed model, it is then compared with grey model (gm) and regression model. the comparison reveals that the mcgm model has higher precision than those of the gm ...

In view of the characteristic that the traffic system is a dynamic and time-varying parameter system, the multi-level recursive forecasting method is proposed, and the multi-level recursive forecasting model of road accidents is established in this thesis. In this method, the forecasting of road accidents is divided into two parts: the forecasting of time-varying parameters and the future forec...

Due to the liberalization of countless electricity markets, load forecasting has become crucial to all public utilities for which electricity is a strategic variable. With the goal of contributing to the forecasting process inside public utilities, this paper addresses the issue of applying the Holt-Winters exponential smoothing technique and the time series analysis for forecasting the hourly ...

The developed software is a web application with open access and is aimed on forecasting of time series stored in database. We proposed approach of time series forecasting, combined ARIMA models with fuzzy techniques: three fuzzy time series models, fuzzy transformation (F-transform) and ACL-scale. Applications of a proposed web service have demonstrated efficiency in practical time series pred...

In our daily life, people are often using forecasting techniques to predict weather, stock, economy and even some important Key Performance Indicator (KPI), and so forth. Therefore, forecasting methods have recently received increasing attention. In the last years, many researchers used fuzzy time series methods for forecasting because of their capability of dealing with vague data. The followe...

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensembl...

The sequential travel forecasting procedure is widely accepted without question by transportation planners, yet its origins are obscure, its effects on practice and research may well be negative, and by focusing attention on individual steps, it tends to impede overall progress in improving forecasting methods. Alternatives to the sequential procedure proposed by researchers over the past 30 ye...

Temperature forecasting and rain forecasting in today's environment is playing a major role in many fields like transportation, tour planning and agriculture. The purpose of this paper is to provide a real time forecasting to the user according to their current position and requirement. The simplest method of forecasting the weather, persistence, relies upon today's conditions to forecast the c...

Load forecasting has always been the essential part of an efficient power system operation and planning. A novel approach based on support vector machines is proposed in this paper for annual power load forecasting. Different kernel functions are selected to construct a combinatorial algorithm. The performance of the new model is evaluated with a real-world dataset, and compared with two neural...