Yearly electricity consumption trends of most developing countries usually exhibit approximately exponential growth curves. An optimized nonhomogeneous exponential model (ONEM) is proposed as a method of forecasting electricity consumption by using trend extrapolation. The parameters of the nonhomogeneous exponential equation are obtained by using the inverse accumulated generating operation, d...

The forecasting of energy consumption in China is a key requirement for achieving national energy security and energy planning. In this study, multi-variable linear regression (MLR) and support vector regression (SVR) were utilized with a gated recurrent unit (GRU) artificial neural network of Chinese energy to establish a forecasting model. The derived model was validated through four economic...

Periodic time series models have become an appealing tool for the analysis of time series showing distinct seasonal patterns. Since these models condition the data{generating mechanism of a given time series on the season they are able to cope with periodic generalisations of common economic models introducing seasonal preferences, seasonal technologies etc. The paper examines for some macroeco...

It has always been an important research objective to improve prediction accuracy with limited data. The Grey Model is a typical example in forecasting industrial water c onsumption with a small amount of data. As to the basic grey forecasting models, the prediction accuracy can still be improved. So this paper proposed an improved new information and equal dimensional grey model. The industria...

In this paper we study the problem of water consumption forecasting, an instance of the general time series forecasting problem, that has not been explored adequately. We base our analysis on two types of data: aggregate and individual consumptions measured by Smart Water Meters. We evaluate a series of state of the art forecasting algorithms and showcase that these models are not suitable for ...

Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine (LSSVM) is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the ...

The logistic model has been very effective in forecasting many technological forecasting patterns. However, it has the characteristic of underestimating the forecasts in many situations. This is mainly due to the constraints imposed by the constant saturation level of the logistic growth curve. This paper proposes a variable asymptote logistic (VAL) model for forecasting electricity consumption...

L. Paliu-Popa, “Constantin Brancusi” University of Targu Jiu, Faculty of Economics and Business Administration, Targu Jiu, Romania Given that the modern world cannot be conceived without the existence of the steel and its use, the crude steel consumption may be considered as an indicator characterizing the economic development of a country. The purpose of this paper is to establish the evolutio...

This paper uses linear and nonlinear statistical models, including artificial neural network (ANN) methods, to investigate the influence of the four economic factors, which are the national income (NI), population (POP), gross of domestic production (GDP), and consumer price index (CPI) on the electricity consumption in Taiwan and then to develop an economic forecasting model. Both methods agre...

This paper use linear regression and non-linear artificial neural network (ANN) model to analyze how the four economic factors: national income (NI), population (POP), gross of domestic production (GDP), and consumer price index (CPI), affect Taiwan’s electricity consumption, furthermore, develop an economic forecasting model. Both models agree with that POP and NI are of the most influence on ...