It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, non-stationary, fluctuations, and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model...

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...

Gas demand possesses dual property of growing and seasonal fluctuation simultaneously, it makes gas demand variation possess complex nonlinear character. From previous studies know single model for nonlinear problem can’t get good results but accurately gas forecast were essential part of an efficient gas system planning and operation. In recent years, lots of scholar put forward combination mo...

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 ...

Forecasting electricity consumption requires the projection of past consumption patterns into the future using a mathematical model. The simplest model for doing this is the univariate model, which has no inputs, and therefore requires no knowledge of future trends in variables affecting consumption. It is well known that input driven (causal) models can have significantly better performance th...