An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction

Energy price time series exhibit nonlinear and nonstationary features, which make accurate forecasting energy prices challenging. In this paper, we propose a novel decomposition-ensemble forecasting paradigm based on ensemble empirical mode decomposition (EEMD) and local linear prediction (LLP). The EEMD is used to decompose energy price time series into components, including several intrinsic mode functions and one residual with a simplified structure. Motivated by the findings of the fully local characteristics of a time series decomposed by the EEMD, we adopt the LLP technique to forecast each component. The forecasting results of all the components are aggregated as a final forecast. For validation, three types of energy price time series, crude oil, electricity and natural gas prices, are studied. The experimental results indicate that the proposed model achieves an improvement in terms of both level forecasting and direction forecasting. The performance of the proposed model is also validated through comparison with several energy price forecasting approaches from the literature. In addition, the robustness and the effects of the parameter settings of LLP are investigated. We conclude the proposed model is easy to implement and efficient for energy price forecasting.     

The long-term trends on the electricity markets: Comparison of empirical mode and wavelet decompositions

This paper proposes an improved approach to electricity prices trend-cyclical component filtering, which is based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). A combined criterion for determining the modes to be included into the trend component is introduced. The performance of the proposed approach is compared with the ordinary empirical mode decomposition (EMD), as well as with the method of wavelet-decomposition well-known in the energy economics literature. We test it on four day-ahead electricity markets: the Europe-Ural and the Siberia price zones of the Russian ATS exchange, the PJM exchange of the USA and the APX exchange of the United Kingdom. Our results show that the proposed approach based on CEEMDAN and the combined criterion outperforms the standard EMD on all the four electricity markets, and on two of the studied markets (PJM, APX) it outperforms the wavelet-smoothing, while on the other two (ATS Europe-Ural and Siberia) it performs at least not worse than the wavelet-smoothing. At the same time, the proposed approach does not require a prior choice of the smoothing parameter, as in the case of the wavelet-decomposition, and demonstrates a certain degree of versatility on the studied markets.     

Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices

An accurate prediction of crude oil prices over long future horizons is challenging and of great interest to governments, enterprises, and investors. This paper proposes a revised hybrid model built upon empirical mode decomposition (EMD) based on the feed-forward neural network (FNN) modeling framework incorporating the slope-based method (SBM), which is capable of capturing the complex dynamic of crude oil prices. Three commonly used multi-step-ahead prediction strategies proposed in the literature, including iterated strategy, direct strategy, and MIMO (multiple-input multiple-output) strategy, are examined and compared, and practical considerations for the selection of a prediction strategy for multi-step-ahead forecasting relating to crude oil prices are identified. The weekly data from the WTI (West Texas Intermediate) crude oil spot price are used to compare the performance of the alternative models under the EMD–SBM–FNN modeling framework with selected counterparts. The quantitative and comprehensive assessments are performed on the basis of prediction accuracy and computational cost. The results obtained in this study indicate that the proposed EMD–SBM–FNN model using the MIMO strategy is the best in terms of prediction accuracy with accredited computational load.     

The VEC-NAR model for short-term forecasting of oil prices

The prediction of future crude oil prices is highly challenging due to three characteristics of crude oil prices, namely, their lag, nonlinearity, and interrelationship among different oil markets, which cannot be handled simultaneously by most traditional crude oil price forecasting models. This paper proposes a new hybrid vector error correction and nonlinear autoregressive neural network (VEC-NAR) model to deal with these characteristics simultaneously. Firstly, a VEC model is used to optimize the lag of crude oil prices and determine the interrelationship which distinguishes the endogenous and exogenous variables. Then, the optimal results obtained by the VEC model are combined with a NAR model which effectively depicts nonlinear component, to forecast crude oil prices. The data of Brent oil prices from January 1, 2003 to December 31, 2014 were used as the empirical sample to test the effectiveness of our proposed model which is compared with those well-recognized methods for crude oil price forecasting. The results of Diebold-Mariano test demonstrated that the VEC-NAR model provided superior forecasting accuracy to traditional models such as GARCH class models, VAR, VEC and NAR model in multi-step ahead short-term forecast.     

Forecasting electricity price and demand using a hybrid approach based on wavelet transform,ARIMA and neural networks

Demand and price forecasting are extremely important for participants in energy markets. Most research work in the area predicts demand and price signals separately. In this paper, a model is presented which predicts electricity demand and price simultaneously. The model combines wavelet transforms, ARIMA models and neural networks. Both time domain and wavelet domain variables are considered in the feature set for price and demand forecasting. The best input set is selected by two‐step correlation analysis. The proposed model is better adapted to real conditions of an energy market since the forecast features for price and demand are not assumed as known values but are predicted by the model, thus accounting for the interactions of the demand and price forecast processes. The forecast accuracy of the proposed method is evaluated using data from the Finnish energy market, which is part of the Nordic Power Pool. The results show that the proposed model provides significant improvement in both demand and price prediction accuracy compared with models using a separate frameworks approach. Copyright © 2013 John Wiley & Sons, Ltd.     

基于EMD-MDT的直流微电网线路故障检测

提出一种经验模态分解和决策树相结合的风光储直流微电网配电线路故障检测和分类方法。该方法通过经验模态分解对发送端测得的直流电流进行分解,得到多个本征模态函数,并采用最大加权相关系数方法计算选取灵敏度最高的2个本征模态函数,将9个统计方法应用于本征模态函数生成故障特征值,最终基于决策树,实现风光储直流微电网配电线路的精准检测和分类。算例分析表明,基于经验模态分解的风光储直流微电网配电线路故障检测方法可不受故障电阻、故障起始时刻和故障距离的影响,快速有效的对配电线故障进行检测和分类。     

Role of carbon swap trading and energy prices in price correlations and volatilities between carbon markets

The present paper theoretically and empirically examines the role of carbon swap trading and energy prices in volatilities and price correlations between the EU and Kyoto Protocol emissions trading schemes. A supply and demand based correlation model between EUA and sCER price returns is proposed in detail using inverse Box–Cox type marginal abatement cost (MAC) curves and simple emission reduction volume processes. The model includes financial players' EUA–sCER swap transaction in boom periods of carbon prices using the logit model for EUA and EUA–sCER swap volume correlations, and stronger energy price impacts on EUA prices than sCER prices using a mean-reverting lognormal process for energy prices. The empirical studies using EUA and sCER prices estimate the model parameters, resulting in a positive EUA volume impact on EUA–sCER swap transactions and a positive energy price impact on EUA prices. It is shown that high EUA–sCER price correlations during high EUA price periods stemmed from EUA–sCER swap transactions, whereas high EUA–sCER price correlations during the period of financial turmoil with low EUA prices came from the drop in energy prices. We also show that the leverage effects often observed in security markets exist in both the EUA and sCER markets according to the price–volatility relation.     

基于能量熵重构与支持向量回归的水电机组状态趋势预测

针对水电机组运行状态趋势预测的问题,提出了一种基于能量熵重构(EER)与支持向量回归(SVR)的混合预测模型。先针对复杂非平稳监测信号,利用快速集成经验模态分解(FEEMD)算法将其分解为多个本征模态函数(IMFs)分量和单个残余分量;然后基于能量熵(EE)理论对各分量进行重构,以有效降低分量的复杂度;最后,将生成的重构本征模态函数(RIMFs)作为SVR的输入,训练模型参数得到最优的SVR,用于预测机组状态发展趋势。与实例对比分析表明,所提混合预测模型具有较高的预测精度,为机组运维策略的制定提供了一定的指导。     

Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

In the context of the liberalized and deregulated electricity markets, price forecasting has become increasingly important for energy company's plans and market strategies. Within the class of the time series models that are used to perform price forecasting, the subclasses of methods based on stochastic time series and causal models commonly provide point forecasts, whereas the corresponding uncertainty is quantified by approximate or simulation-based confidence intervals. Aiming to improve the uncertainty assessment, this study introduces the Generalized Additive Models for Location, Scale and Shape (GAMLSS) to model the dynamically varying distribution of prices. The GAMLSS allow fitting a variety of distributions whose parameters change according to covariates via a number of linear and nonlinear relationships. In this way, price periodicities, trends and abrupt changes characterizing both the position parameter (linked to the expected value of prices), and the scale and shape parameters (related to price volatility, skewness, and kurtosis) can be explicitly incorporated in the model setup. Relying on the past behavior of the prices and exogenous variables, the GAMLSS enable the short-term (one-day ahead) forecast of the entire distribution of prices. The approach was tested on two datasets from the widely studied California Power Exchange (CalPX) market, and the less mature Italian Power Exchange (IPEX). CalPX data allow comparing the GAMLSS forecasting performance with published results obtained by different models. The study points out that the GAMLSS framework can be a flexible alternative to several linear and nonlinear stochastic models.     

Monthly crude oil spot price forecasting using variational mode decomposition

Crude oil is one of the most important trade commodities in the world and its price fluctuation has significant effects on global economic activities. In this paper, we proposed hybrid models for monthly crude oil price forecasting using variational mode decomposition and artificial intelligence (AI) techniques (in this paper, support vector machine optimized by genetic algorithm (GASVM) and back propagation neural network optimized by genetic algorithm (GABP) are employed for analyzing). In addition, influencing factors of the long-term crude oil price such as the global crude oil production as well as economic activity (Dow Jones Industrial Index is considered in this paper) are investigated and considered on the crude oil price forecasting. Empirical forecasting results of monthly West Texas Intermediate (WTI) and Brent crude oil spot prices validate that the hybrid VMD-based models are superior to previously popular EEMD-based models and single models in terms of both level and directional prediction accuracies as well as the DM test results. In addition, the VMD-AI based models which consider influencing factors of the long-term crude oil price variation perform better than that without considering influencing factors of the long-term crude oil price variation in terms of MAPE and RMSE. All of which confirm that the newly proposed VMD-AI based models are promising tools for crude oil price analysis and forecasting.     

Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae

We consider the problem of modelling and forecasting the distribution of a vector of prices from interconnected electricity markets using a flexible class of drawable vine copula models, where we allow the dependence parameters of the constituting bivariate copulae to be time-varying. We undertake in-sample and out-of-sample tests using daily electricity prices, and evidence that our model provides accurate forecasts of the underlying distribution and outperforms a set of competing models in their abilities to forecast one-day-ahead conditional quantiles of a portfolio of electricity prices. Our study is conducted in the Australian National Electricity Market (NEM), which is the most efficient power auction in the world. Electricity prices exhibit highly stylised features such as extreme price spikes, price dependency between regional markets, correlation asymmetry and non-linear dependency. The developed approach can be used as a risk management tool in the electricity retail industry, which plays an integral role in the apparatus of modern energy markets. Electricity retailers are responsible for the efficient distribution of electricity, while being exposed to market risk with extreme magnitudes.     

基于CEEMDAN+RF+AdaBoost的短期负荷预测

高精度的短期负荷预测不仅是电力系统运行稳定的关键,也是构建智能电网的必要保证。为提高电力系统短期负荷预测精度,提出了一种基于完整集成经验模态分解(CEEMDAN)、随机森林(RF)和AdaBoost的预测方法。针对传统分解方法不能完整分解原始负荷序列的问题,利用CEEMDAN分解方法为各个阶段的IMF分解信号添加特定的白噪声,通过计算余量信号来获得各个模态分量,然后针对前9个模态分量构建RF预测模型,针对残余量构建AdaBoost预测模型,并对结果进行重构预测,得出未来24h的负荷预测数据。最后将CEEMDAN+RF+AdaBoost方法应用于华中地区的短期负荷预测,在同等条件下,与预测模型CEEMDAN+RF、EEMD+RF+AdaBoost、EMD+RF+AdaBoost、RF及AdaBoost进行试验对比,结果表明所构建预测模型的精度优于其他对比模型,具有很好的理论指导意义和实际应用前景。     

基于EEMD-ANN的水库年径流预测

以三峡水库为例,基于集合经验模态分解(EEMD)及人工神经网络方法对水库年径流进行预测。首先利用Mann-Kendall和Pettitt法对水库年径流序列进行突变检测,获得平稳径流序列,然后采取EEMD方法分解径流序列,得到固有模态函数(IMF)和残差,最后对不同IMFs和残差分别建立人工神经网络预测模型,叠加所有模型预测结果得到年径流预测值。结果表明,基于EEMD-ANN的年径流预测模型优于自回归模型和人工神经网络模型,其预测结果与实测值的相关性更强,预测误差分别减少了11.4%、8.7%。同时,构建EEMD-ANN预测模型时需考虑径流序列的突变特征,采取平稳径流序列的预测效果更优。     

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique

This paper proposes the day-ahead electricity price forecasting using the artificial neural networks (ANN) and weighted least square (WLS) technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices (LMP) in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach proposed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland (PJM) interconnection is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.     

Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

Wind speed is the major factor that affects the wind generation, and in turn the forecasting accuracy of wind speed is the key to wind power prediction. In this paper, a wind speed forecasting method based on improved empirical mode decomposition (EMD) and GA-BP neural network is proposed. EMD has been applied extensively for analyzing nonlinear stochastic signals. Ensemble empirical mode decomposition (EEMD) is an improved method of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each signal is taken as an input data to the GA-BP neural network model. The final forecasted wind speed data is obtained by aggregating the predicted data of individual signals. Cases study of a wind farm in Inner Mongolia, China, shows that the proposed hybrid method is much more accurate than the traditional GA-BP forecasting approach and GA-BP with EMD and wavelet neural network method. By the sensitivity analysis of parameters, it can be seen that appropriate settings on parameters can improve the forecasting result. The simulation with MATLAB shows that the proposed method can improve the forecasting accuracy and computational efficiency, which make it suitable for on-line ultra-short term (10 min) and short term (1 h) wind speed forecasting.     

On the short- and long-run efficiency of energy and precious metal markets

This article contributes to the related literature by empirically investigating the efficiency of nine energy and precious metal markets over the last decades, employing several pronounced models. We test for both short- and the long-run efficiency using, in addition to linear cointegration models, nonlinear cointegration and error-correction models (ECMs) which allow the efficiency intensity to change per regime. Our findings can be summarized as follows: i) futures prices are found to be cointegrated with spot prices, but they do not constitute unbiased predictors of future spot prices; ii) the hypothesis of risk neutrality is rejected; iii) the short-run efficiency hypothesis is rejected, suggesting that using past futures price returns improves the modeling and forecasting of future spot prices; and iv) the nonlinear modeling suggests the presence of two distinct regimes wherein the first regime the efficiency hypothesis is supported, whereas in the second it is rejected. The empirical findings have important implications for producers, hedgers, speculators and policymakers.     

A model of carbon price interactions with macroeconomic and energy dynamics

This paper develops a model of carbon pricing by considering two fundamental drivers of European Union Allowances: economic activity and energy prices. On the one hand, economic activity is proxied by aggregated industrial production in the EU 27 (as it provides the best performance in a preliminary forecasting exercise vs. other indicators). On the other hand, brent, natural gas and coal prices are selected as being the main carbon price drivers (as highlighted by previous literature). The interactions between the macroeconomic and energy spheres are captured in a Markov-switching VAR model with two states that is able to reproduce the ‘boom–bust’ business cycle (Hamilton (1989)). First, industrial production is found to impact positively (negatively) the carbon market during periods of economic expansion (recession), thereby confirming the existence of a link between the macroeconomy and the price of carbon. Second, the brent price is confirmed to be the leader in price formation among energy markets (Bachmeier and Griffin (2006)), as it impacts other variables through the structure of the Markov-switching model. Taken together, these results uncover new interactions between the recently created EU emissions market and the pre-existing macroeconomic/energy environment.     

Exogenous impacts on the links between energy and agricultural commodity markets

The main purpose of this paper is to identify the effects of exogenous factors, which have been somewhat controversial, on the price links between the energy and agricultural commodity markets. Our study differs from other studies by employing multivariate normal mixture models to capture the structural properties of the price dependencies in the underlying states. This paper investigates price dependencies from both quantitative and structural perspectives. By analyzing the overall dependencies and structural heterogeneity in the empirical results, we conclude that the global financial crisis is the most influential shock on the price links between energy and agricultural commodities. Because price links are vulnerable to financial shocks, our results also suggest introducing state-based analysis to risk management and portfolio diversification across the energy and agriculture markets during times of turmoil.     

基于EMD-BPNN的水电机组空蚀故障诊断

针对水电机组空蚀信号非平稳和非线性的特点,提出一种基于经验模态分解-BP神经网络(EMDBPNN)的空蚀故障混合特征提取与分类方法。首先对空蚀信号进行经验模态分解,得到一系列的本征模态函数(IMFs),提取各IMFs分量的能量特征和奇异值特征,同时提取常规的时域和频域特征,构建混合特征向量;然后将此向量作为神经网络的输入,对水电机组空载工况、导叶30%开度和满负荷运行等三种工况下的空蚀数据进行识别分类。试验结果显示,该方法能够对水电机组空蚀故障进行准确诊断,具有较强的工程应用价值。