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This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electric consumption prediction. Six models are proposed to forecast annual Electric demand. 104 ANFIS have been constructed and tested in order to finding best ANFIS for Electric consumption. The proposed models consist of input variables such as Gross Domestic Product (GDP) and Population (POP). All...
Logistics demand forecasting is important for investment decision-making of infrastructure and strategy programming of the logistics industry. In this paper, a hybrid method which combines the Grey Model, artificial neural networks and other techniques in both learning and analyzing phases is proposed to improve the precision and reliability of forecasting. After establishing a learning model GNNM(1,8)...
Based on the actual urban residential water demand of Xi'an, the Radial Basis Function (RBF) artificial neural network was used to forecast the urban residential water demand. RBF artificial neural network model was employed based on two input variables of population and Gross Domestic Product (GDP), one output variable of urban residential water demand. The performances in RBF forecasting of different...
This paper according to the intrinsic relationship between demand land for construction and socio-economic development indicators, presents a models to forecast the demand for construction: a multi-parameter nonlinear model for forecasting demand for construction.In the multi-parameter nonlinear model, population, GDP, financial expenditure and total investment in fixed assets, were selected as the...
Linear regression has been used for many years for forecasting in marketing, management, sales and energy. In this paper, a fuzzy-based approach is applied for the transport energy demand forecasting using socio-economic and transport related indicators. This forecasting is analyzed based on gross domestic product (GDP), population and the number of vehicles together with historical energy data from...
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