Integration of genetic algorithm,computer simulation and design of experiments for forecasting electrical energy consumption

This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on genetic algorithm (GA), computer simulation and design of experiments using stochastic procedures. First, time-series model is developed as a benchmark for GA and simulation. 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 GA and simulated-based GA models are then developed by the selected time-series model. Therefore, there are four treatments to be considered in analysis of variance (ANOVA) which are actual data, time series, GA and simulated-based GA. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being 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 Multiple Range Test (DMRT) method of paired comparison is used to select the optimum model, which could be time series, GA or simulated-based GA. 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-fit GA 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 GA 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.     

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.     

A seasonal hybrid procedure for electricity demand forecasting in China

Electricity is a special energy which is hard to store, so the electricity demand forecasting in China remains an important problem. This paper aims at developing an improved hybrid model for electricity demand in China, which takes the advantages of moving average procedure, combined method, hybrid model and adaptive particle swarm optimization algorithm, known as MA-C-WH. It is designed for making trend and seasonal adjustments which simultaneously presents the electricity demand forecasts. Four actual electricity demand time series in China power grids are selected to illustrate the proposed MA-C-WH model, and one existing seasonal autoregressive integrated moving average model (SARIMA) is selected to compare with the proposed model using the same data series. The results of popular forecasting precision indexes show that our proposed model is an effective forecasting technique for seasonal time series with nonlinear trend.     

智能楼宇用电负荷优化调度

现阶段中国建筑能源消耗较大,浪费现象频现,同时,光伏等新能源分布式发电在用户侧大力推广,如何在优先消纳分布式发电的情况下管理大型楼宇用电,节约电量,降低电费,成为了近年智能楼宇建设的关键问题.对智能楼宇用电管理系统的分层架构体系进行了简单介绍,并通过对用户负荷分类,以用电经济性、舒适性和电网稳定性为目标提出了负荷优化调...     

基于T-S模型的质子交换膜燃料电池控制建模

对PEMFC非线性复杂被控对象,提出了一种在线辨识模糊预测算法,用模糊聚类和线性辨识方法在线建立PEMFC控制系统的T—S模糊预测模型,仿真实验结果表明了该模糊辨识建模方法具有建模简单、模型精度高等优点,亦证明了该算法的有效性和优越性。研究结果对质子交换膜燃料电池控制系统的建模和控制具有一定的实用价值。     

Electricity consumption and associated GHG emissions of the Jordanian industrial sector: Empirical analysis and future projection

In this paper, an empirical model is developed for electricity consumption of the Jordanian industrial sector based on multivariate linear regression to identify the main drivers behind electricity consumption. In addition, projection of electricity consumption for the industrial sector based on time series forecasting is presented. It was found that industrial production outputs and capacity utilization are the two most important variables that affect demand on electrical power and the multivariate linear regression model can be used adequately to simulate industrial electricity consumption with very high coefficient of determination. To illustrate the importance of integrating energy efficiency within national energy plans, the impact of implementing high-efficiency motors was investigated and found to be significant. Without such basic energy conservation and management programs, electricity consumption and associated GHG emissions for the industrial sector are predicted to rise by 63% in the year 2019. However, if these measures are implemented on a gradual basis, over the same period, electricity consumption and GHG emissions are forecasted to ascend at a lower rate with low/no cost actions.     

A flexible fuzzy regression algorithm for forecasting oil consumption estimation

Oil consumption plays a vital role in socio-economic development of most countries. This study presents a flexible fuzzy regression algorithm for forecasting oil consumption based on standard economic indicators. The standard indicators are annual population, cost of crude oil import, gross domestic production (GDP) and annual oil production in the last period. The proposed algorithm uses analysis of variance (ANOVA) to select either fuzzy regression or conventional regression for future demand estimation. The significance of the proposed algorithm is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and minimum absolute percentage error (MAPE), whereas previous studies consider the best fitted fuzzy regression model based on MAPE or other relative error results. Second, the proposed model may identify conventional regression as the best model for future oil consumption forecasting because of its dynamic structure, whereas previous studies assume that fuzzy regression always provide the best solutions and estimation. Third, it utilizes the most standard independent variables for the regression models. To show the applicability and superiority of the proposed flexible fuzzy regression algorithm the data for oil consumption in Canada, United States, Japan and Australia from 1990 to 2005 are used. The results show that the flexible algorithm provides accurate solution for oil consumption estimation problem. The algorithm may be used by policy makers to accurately foresee the behavior of oil consumption in various regions.     

Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input–output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export–import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption.     

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.     

A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA,Canada, Singapore,Pakistan and Iran

Utilization of small data sets for energy consumption forecasting is a major problem because it could create large noise. This study presents a hybrid framework for improvement of energy consumption estimation with small data sets. The framework is based on fuzzy regression, conventional regression and design of experiment (DOE). The hybrid framework uses analysis of variance (ANOVA) and minimum absolute percentage error (MAPE) to select between fuzzy and conventional regressions. The significance of the proposed framework is three fold. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE. Second, the framework may identify conventional regression as the best model for future energy consumption forecasting because of its dynamic structure, whereas in the case of uncertainty and ambiguity, previous studies assume that fuzzy regression provides better solutions and estimation. Third, it is ideal candidate for short data sets. To show the applicability of the hybrid framework, the data for energy consumption in Canada, United States, Singapore, Pakistan and Iran from 1995 to 2005 are considered and tested. This is the first study which introduces a hybrid fuzzy regression-design of experiment for improvement of energy consumption estimation and forecasting with relatively small data sets.     

Time series models (Grey-Markov,Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India

The present study applies three time series models, namely, Grey-Markov model, Grey-Model with rolling mechanism, and singular spectrum analysis (SSA) to forecast the consumption of conventional energy in India. Grey-Markov model has been employed to forecast crude-petroleum consumption while Grey-Model with rolling mechanism to forecast coal, electricity (in utilities) consumption and SSA to predict natural gas consumption. The models for each time series has been selected by carefully examining the structure of the individual time series. The mean absolute percentage errors (MAPE) for two out of sample forecasts have been obtained as follows: 1.6% for crude-petroleum, 3.5% for coal, 3.4% for electricity and 3.4% for natural gas consumption. For two out of sample forecasts, the prediction accuracy for coal consumption was 97.9%, 95.4% while for electricity consumption the prediction accuracy was 96.9%, 95.1%. Similarly, the prediction accuracy for crude-petroleum consumption was found to be 99.2%, 97.6% while for natural gas consumption these values were 98.6%, 94.5%. The results obtained have also been compared with those of Planning Commission of India's projection. The comparison clearly points to the enormous potential that these time series models possess in energy consumption forecasting and can be considered as a viable alternative.     

Testing for nonlinearity in solar radiation time series by a fast surrogate data test method

Recently, various time series models have been proposed to predict solar radiation, for instance the ARIMA (autoregressive integrated moving average) model and neural networks. Before building a model for the data, however, it is advisable to check whether the data suggest this type of modeling. More specifically, a nonlinearity test is suggested before further analysis with the linear or nonlinear tools are to be applied. In this paper, we test the presence of nonlinearity in the solar radiation time series by the method of surrogate data. The surrogate test method used in this paper is based on evaluation of the differences between the original time series and the linear model that best approximates it. Nonlinearity tests are carried out for four data sets including 5-min, hourly, daily and monthly global solar radiation time series from the UO (University of Oregon) Solar Radiation Monitoring Laboratory. The test statistics show that the 5-min, hourly, daily global solar radiation time series exhibit apparently nonlinearity while the monthly time series does not.     

Energy consumption in the residential sector: a study on critical factors

In recent years, lots of efforts have been devoted to the identification of the factors influencing residential energy consumption. Many factors affect energy consumption at the same time, leading to the lack of precision when identifying which factors are significant. This paper reports the results of performing factor analysis for examining the factors affecting residential energy consumption. Data gathered through interviews and surveys with the residents and of housing units in Tehran (capital of Iran) are used for this purpose. The database applied comprises 56 predictors, for 2087 observations. Thirteen latent factors related to households’ energy consumption were shown by the data. Finally, a regression model was employed in order to recognise the most important factors. The amount of electricity and natural gas consumption was used as the dependent variable in the regression model. The results obtained can help prioritise efforts for modifying parameters in order to reduce the energy consumption in the residential sector.     

An integrated DEA–COLS–SFA algorithm for optimization and policy making of electricity distribution units

This paper presents an integrated data envelopment analysis (DEA)–corrected ordinary least squares (COLS)–stochastic frontier analysis (SFA)–principal component analysis (PCA)–numerical taxonomy (NT) algorithm for performance assessment, optimization and policy making of electricity distribution units. Previous studies have generally used input–output DEA models for benchmarking and evaluation of electricity distribution units. However, this study proposes an integrated flexible approach to measure the rank and choose the best version of the DEA method for optimization and policy making purposes. It covers both static and dynamic aspects of information environment due to involvement of SFA which is finally compared with the best DEA model through the Spearman correlation technique. The integrated approach would yield in improved ranking and optimization of electricity distribution systems. To illustrate the usability and reliability of the proposed algorithm, 38 electricity distribution units in Iran have been considered, ranked and optimized by the proposed algorithm of this study.     

计及购电成本的电网交易能力研究

在电力市场环境下,考虑购电成本对电网交易能力的影响,建立兼顾电网交易能力和购电成本的多目标交易优化模型,运用模糊建模的方法将多目标优化问题转化为求解最大满意度的单目标非线性规划问题,采用原对偶内点法求解。通过对IEEE-30节点测试系统进行仿真计算,结果表明所提出的多目标交易优化模型,可以在提高交易能力的同时尽量减小购电成本,验证了模型及算法的有效性。     

Disentangling temporal patterns in elasticities: A functional coefficient panel analysis of electricity demand

We introduce a panel model with a nonparametric functional coefficient of multiple arguments. The coefficient is a function both of time, allowing temporal changes in an otherwise linear model, and of the regressor itself, allowing nonlinearity. In contrast to a time series model, the effects of the two arguments can be identified using a panel model. We apply the model to the relationship between real GDP and electricity consumption. Our results suggest that the corresponding elasticities have decreased over time in developed countries, but that this decrease cannot be entirely explained by changes in GDP itself or by sectoral shifts.     

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.     

Investigating the need of nuclear power plants for sustainable energy in Iran

Over the decades, the consumption of all types of energy such as electricity increased rapidly in Iran. Therefore, the government decided to redevelop its nuclear program to meet the rising electricity demand and decrease consumption of fossil fuels. In this paper, the effect of this policy in four major aspects of energy sustainability in the country, including energy price, environmental issues, energy demand and energy security have been verified. To investigate the relative cost of electricity generated in each alternative generator, the simple levelized electricity cost was selected as a method. The results show that electricity cost in fossil fuel power plants presumably will be cheaper than nuclear. Although the usage of nuclear reactor to generate power is capable of decreasing hazardous emissions into the environment, there are many other effective policies and technologies that can be implemented. Energy demand growth in the country is very high; neither nuclear nor fossil fuel cannot currently cope with the growth. So, the only solution is rationalizing energy demand by price amendment and encouraging energy efficiency. The major threats of energy security in Iran are high energy consumption growth and economic dependency on crude oil export. Though nuclear energy including its fuel cycle is Iran's assured right, constructing more nuclear power plants will not resolve the energy sustainability problems. In fact, it may be the catalyst for deterioration since it will divert capital and other finite resources from top priority and economic projects such as energy efficiency, high technology development and energy resources management.     

基于粒子群—高斯过程回归的高拱坝变形预测模型及其应用

针对高拱坝变形问题,提出应用粒子群算法优化高斯过程回归参数的高拱坝变形预测模型,基于高斯过程回归可将低维非线性关系通过核函数投射到高维线性空间的特点,利用高斯过程回归模型来表征水压、温度、时效等因素与坝体变形之间的非线性关系;同时针对迭代求解高斯过程回归模型的超参数效率低的问题,采用粒子群优化算法全局搜索模型超参数,提高了求解效率。对某高拱坝径向位移的拟合预测结果表明,粒子群优化高斯过程回归模型能较好地表征输入因子与变形之间的关系,预测坝体变形,误差在工程允许范围内,可应用于坝体变形预测分析中。     

线性回归在供电标煤耗分析中的应用

供电标煤耗是评价热电联产企业综合竞争能力的重要指标之一。文中根据某企业供电标煤耗与热电比、汽轮机效率、锅炉效率的对应数据资料,将供电标煤耗与三者之间的变动关系加以模型化,找出并检验回归方程,尝试用回归分析的方法对供电标煤耗进行预测,为热电联产企业降低煤耗,提高市场竞争能力提供依据。