Forecasting energy consumption in Taiwan using hybrid nonlinear models

The total consumption of electricity and petroleum energies accounts for almost 90% of the total energy consumption in Taiwan, so it is critical to model and forecast them accurately. For univariate modeling, this paper proposes two new hybrid nonlinear models that combine a linear model with an artificial neural network (ANN) to develop adjusted forecasts, taking into account heteroscedasticity in the model's input. Both of the hybrid models can decrease round-off and prediction errors for multi-step-ahead forecasting. The results suggest that the new hybrid model generally produces forecasts which, on the basis of out-of-sample forecast encompassing tests and comparisons of three different statistic measures, routinely dominate the forecasts from conventional linear models. The superiority of the hybrid ANNs is due to their flexibility to account for potentially complex nonlinear relationships that are not easily captured by linear models. Furthermore, all of the linear and nonlinear models have highly accurate forecasts, since the mean absolute percentage forecast error (MAPE) results are less than 5%. Overall, the inclusion of heteroscedastic variations in the input layer of the hybrid univariate model could help improve the modeling accuracy for multi-step-ahead forecasting.     

Electricity consumption forecasting in Italy using linear regression models

The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model.The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population.A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to −0.06, while long run elasticities are equal to −0.24 and −0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values.In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models.A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account.     

Forecast of electricity consumption and economic growth in Taiwan by state space modeling

This paper investigates the Granger causality between electricity consumption (EL) and economic growth for Taiwan during 1980–2007 using the cointegration and error-correction models. The results indicate that EL and real GDP are cointegrated, and that there is unidirectional short and long run Granger causality from economic growth to EL but not vice versa. Considering cointegrated property, this study proposes a new error-correction state space model (ECSTSP) with the error-correction term (ECT) in its state vector to forecast both EL and real GDP simultaneously, whereas the ECM is not in the state vector of classical state space model (STSP). The out-of-sample forecasting ability of the ECSTSP is compared with STSP and SARIMA models using six forecasting horizons from 1-year to 6-year. The results suggest that all of the models have strong forecasting performance with MAPE less than 5.4%, but the ECSTSPs have the smallest average values of MAPEs for both EL and GDP, which are 2.50% and 1.74%, respectively. For short-term predictions, SARIMA models are as good as STSP or ECSTSP ones. For long-term prediction, ECSTSP is the best model, because the cointegration relationship between real GDP and EL is taken into account in this model.     

基于GDP-电能消费耦合关系的中长期电量预测

建立了一种基于用电量和GDP之间耦合关系的中长期电量预测模型。首先利用协整检验和格兰杰（Granger）因果检验,剖析电能消费和经济发展之间的协整关系和因果关系,并建立中长期电量预测模型。然后采用误差修正方法对预测模型进行短期调节,以提高模型的鲁棒性以及预测精度。以某地区1991—2015年的用电量和GDP数据作为算例输入数据,结果表明：通过构建电能消费和经济发展之间的耦合关系,有助于提高预测模型的解释能力,同时含短期调节的中长期用电量预测模型具有更高的预测精度。     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.     

Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches

This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting in Lebanon. In order to provide the forecasted energy consumption, the ANN interpolates among the EEC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on EEC time series and a weather‐dependent variable, namely, degree days (DD). The third model is also a multivariate model based on EEC and a gross domestic product (GDP) proxy, namely, total imports (TI). Finally, the fourth model combines EEC, DD and TI. Forecasting performance measures such as mean square errors (MSE), mean absolute deviations (MAD), mean percentage square errors (MPSE) and mean absolute percentage errors (MAPE) are presented for all models. Copyright © 2002 John Wiley & Sons, Ltd.     

Modeling of electricity consumption in the Asian gaming and tourism center—Macao SAR,People's Republic of China

The use of electricity is indispensable to modern life. As Macao Special Administrative Region becomes a gaming and tourism center in Asia, modeling the consumption of electricity is critical to Macao's economic development. The purposes of this paper are to conduct an extensive literature review on modeling of electricity consumption, and to identify key climatic, demographic, economic and/or industrial factors that may affect the electricity consumption of a country/city. It was identified that the five factors, namely temperature, population, the number of tourists, hotel room occupancy and days per month, could be used to characterize Macao's monthly electricity consumption. Three selected approaches including multiple regression, artificial neural network (ANN) and wavelet ANN were used to derive mathematical models of the electricity consumption. The accuracy of these models was assessed by using the mean squared error (MSE), the mean squared percentage error (MSPE) and the mean absolute percentage error (MAPE). The error analysis shows that wavelet ANN has a very promising forecasting capability and can reveal the periodicity of electricity consumption.     

A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to the proposed algorithm.     

电力消费与经济发展关系国际实证分析——基于长期历史数据的考察

实证分析表明,不论以市场汇率法还是以购买力平价法表示,从长期视角审视,英国、美国、日本人均电力消费与人均GDP的长期历史趋势呈现出快速增长和慢速增长两个不同的阶段,韩国仍然处于快速增长的第一阶段,而且定量分析表明人均电力消费与人均GDP在这两个阶段都具有较高的线性拟合优度;英国、美国、日本电力强度的长期历史趋势呈现倒"V"形模式,韩国电力强度仍然处于倒"V"形的上升阶段;电力消费长期趋势的转变与这些国家经济结构的调整有着重要联系。     

Electric energy demands of Turkey in residential and industrial sectors

The main objective of the present study is to apply the artificial neural network (ANN) methodology, linear regression (LR) and nonlinear regression (NLR) models to estimate the electricity consumptions of the residential and industrial sectors in Turkey. Installed capacity, gross electricity generation, population and total subscribership were selected as independent variables. Two different scenarios (powerful and poor) were proposed for prediction of the future electricity consumption. Obtained results of the LR, NLR and ANN models were also compared with each other as well as the projection of the Ministry of Energy and Natural Resources (MENR) and the results in literature. Results of the comparison showed that the performance values of the ANN method are better than the performance values of the LR and NLR models. According to the poor scenario and ANN model, Turkey's residential and industrial sector electricity consumptions will increase to value of 140.64 TWh and 124.85 TWh by 2015, respectively.     

An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran

This study presents an integrated fuzzy regression and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression (FR) or time series and the integrated algorithm could be an ideal substitute for such cases. At First, preferred Time series model is selected from linear or nonlinear models. For this, after selecting preferred Auto Regression Moving Average (ARMA) model, Mcleod-Li test is applied to determine nonlinearity condition. When, nonlinearity condition is satisfied, the preferred nonlinear model is selected and defined as preferred time series model. At last, the preferred model from fuzzy regression and time series model is selected by the Granger-Newbold. Also, the impact of data preprocessing on the fuzzy regression performance is considered. Monthly electricity consumption of Iran from March 1994 to January 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with other intelligent tools such as Genetic Algorithm (GA) and Artificial Neural Network (ANN).     

Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

The most important theme in this study is to obtain equations based on economic indicators (gross national product—GNP and gross domestic product—GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968–2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R² values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections.     

Energy consumption projection of Nepal: An econometric approach

In energy dependent economies, energy consumption is often linked with the growth in Gross Domestic Product (GDP). Energy intensity, defined herewith, as the ratio of the total primary energy consumption (TPE) to the GDP, is a useful concept for understanding the relation between energy demand and economic development. The scope of this article is to assess the future primary energy consumption of Nepal, and the projection is carried out along with the formulation of simple linear logarithmic energy consumption models. This initiates with a hypothesis that energy consumption is dependent with the national macro-economic parameters. To test the hypothesis, nexus between energy consumption and possible determinant variables are examined. Status of energy consumption between the period of 1996 and 2009, and for the same period, growth of economic parameters are assessed. Three scenarios are developed differing from each other on the basis of growth rates of economic indicators: total GDP, GDP-agriculture, GDP-trade, GDP-industry, and other variables including growth in private consumptions, population, transport vehicles numbers, prices of fossil fuels etc. Scenarios are: Business as Usual (BAU), Medium Growth Scenario (MGS) and High Growth Scenario (HGS). Energy consumption in all the sectors and for all fuel types are not statistically correlated with every economic parameters tested in the assessment. Hence, the statistically correlated models are included in the prognosis of energy consumption. For example, the TPE consumption and electricity consumption, both are significantly dependent with the total GDP and population growth. Likewise, fuel wood consumption is significantly dependent with the growth in rural population and private consumptions. In BAU the estimated electricity consumption in 2030 would be 7.97 TWh, which is 3.47 times higher than that of 2009. In MGS, the total electricity consumption in 2030 is estimated to increase by a factor of 5.71 compared to 2009. Likewise, in HGS, electricity consumption would increase by 10-fold until 2030 compared to 2009, demanding installed capacity of power plant at 6600 MW, which is only from hydro power and other centralised system.     

Structural breaks, energy consumption, and economic growth revisited: Evidence from Taiwan

This paper studies the stability between energy consumption and GDP for Taiwan during 1954–2003. We use aggregate as well as various disaggregate data of energy consumption, including coal, oil, gas, and electricity, to employ the unit root tests and the cointegration tests allowing for structural breaks. Our main findings are: First, though gas consumption seems to have structural breaks in the 1960s, after considering the structural breaks, the series is a stationary variable when Taiwan adopted its expansionary export trade policy. Second, we find that different directions of causality exist between GDP and various kinds of energy consumption. The empirical result shows unanimously in the long run that energy acts as an engine of economic growth, and that energy conservation may harm economic growth. Third, the cointegration between energy consumption and GDP is unstable, and some economic events may affect the stability. Overall, we do find the structural breakpoints, and they look to match clearly with the corresponding critical economic incidents.     

The impact of energy consumption on economic growth: Evidence from linear and nonlinear models in Taiwan

This paper considers the possibility of both a linear effect and nonlinear effect of energy consumption on economic growth, using data for the period 1955–2003 in Taiwan. We find evidence of a level-dependent effect between the two variables. Allowing for a nonlinear effect of energy consumption growth sheds new light on the explanation of the characteristics of the energy-growth link. We also provide evidence that the relationship between energy consumption and economic growth in Taiwan is characterized by an inverse U-shape. Some previous studies support the view that energy consumption may promote economic growth. However, the conclusion drawn from the empirical findings suggests that such a relationship exists only where there is a low level of energy consumption in Taiwan. We show that a threshold regression provides a better empirical model than the standard linear model and that policy-makers should seek to capture economic structures associated with different stages of economic growth. It is also worth noting that the energy consumption threshold was reached in the case of Taiwan in the world energy crises periods of 1979 and 1982.     

Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey

This paper presents Turkey's net electricity energy generation and demand based on economic indicators. Forecasting model for electricity energy generation and demand is first proposed by the ant colony optimization (ACO) approach. It is multi-agent system in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. Ant colony optimization electricity energy estimation (ACOEEE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear electricity energy generation and demand (linear_ACOEEGE and linear ACOEEDE) and quadratic energy generation and demand (quadratic_ACOEEGE and quadratic ACOEEDE). Quadratic models for both generation and demand provided better fit solution due to the fluctuations of the economic indicators. The ACOEEGE and ACOEEDE models indicate Turkey's net electricity energy generation and demand until 2025 according to three scenarios.     

Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables

This study deals with the modeling of the energy consumption in Turkey in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, population, import and export amounts, and employment) using artificial neural network (ANN) and regression analyses. For this purpose, four diverse models including different indicators were used in the analyses. As the result of the analyses, this research proposes Model 2 as a suitable ANN model (having four independent variables being GDP, population, the amount of import and export) to efficiently estimate the energy consumption for Turkey. The proposed model predicted the energy consumption better than the regression models and the other three ANN models. Thus, the future energy consumption of Turkey is calculated by means of this model under different scenarios. The predicted forecast results by ANN were compared with the official forecasts. Finally, it was concluded that all the scenarios that were analyzed gave lower estimates of the energy consumption than the MENR projections and these scenarios also showed that the future energy consumption of Turkey would vary between 117.0 and 175.4 Mtoe in 2014.     

Wind speed forecasting in three different regions of Mexico,using a hybrid ARIMA–ANN model

In this paper the wind speed forecasting in the Isla de Cedros in Baja California, in the Cerro de la Virgen in Zacatecas and in Holbox in Quintana Roo is presented. The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. In order to do wind speed forecasting Hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANN) models were developed. The ARIMA models were first used to do the wind speed forecasting of the time series and then with the obtained errors ANN were built taking into account the nonlinear tendencies that the ARIMA technique could not identify, reducing with this the final errors. Once the Hybrid models were developed 48 data out of sample for each one of the sites were used to do the wind speed forecasting and the results were compared with the ARIMA and the ANN models working separately. Statistical error measures such as the mean error (ME), the mean square error (MSE) and the mean absolute error (MAE) were calculated to compare the three methods. The results showed that the Hybrid models predict the wind velocities with a higher accuracy than the ARIMA and ANN models in the three examined sites.     

BP神经网络模型结构对漫湾径流预报精度的影响研究

以云南省漫湾水电站历史径流状况为研究对象，运用三层前馈反向传播神经网络模型对径流进行中长期预报。为解决神经网络预报模型结构难以确定的问题，尝试在预报过程中通过改变该网络模型的结构并对得到的结果进行比较，从而找到适合该径流序列的最佳神经网络模型结构。实际应用表明，使用该结构的模型在实际预报过程中取得了良好的效果。     

Combining solar irradiance measurements,satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting

Isolated power systems need to generate all the electricity demand with their own renewable resources. Among the latter, solar energy may account for a large share. However, solar energy is a fluctuating source and the island power grid could present an unstable behavior with a high solar penetration. Global Horizontal Solar Irradiance (GHI) forecasting is an important issue to increase solar energy production into electric power system. This study is focused in hourly GHI forecasting from 1 to 6 h ahead. Several statistical models have been successfully tested in GHI forecasting, such us autoregressive (AR), autoregressive moving average (ARMA) and Artificial Neural Networks (ANN). In this paper, ANN models are designed to produce intra-day solar forecasts using ground and exogenous data. Ground data were obtained from two measurement stations in Gran Canaria Island. In order to improve the results obtained with ground data, satellite GHI data (from Helioclim-3) as well as solar radiation and Total Cloud Cover forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as additional inputs of the ANN model. It is shown that combining exogenous data (satellite and ECMWF forecasts) with ground data further improves the accuracy of the intra-day forecasts.