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Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 60283

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Prof. Dr. Wei-Chiang Hong

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Department of Information Management, Asia Eastern University of Science and Technology, Taipei 22064, Taiwan
Interests: short-term load forecasting; intelligent forecasting technologies (e.g., neural networks, knowledge–based expert systems, fuzzy inference systems, evolutionary computation, etc.); hybrid forecasting models (e.g., hybridizing traditional models with intelligent technologies, or hybridizing two or more different models to form a novel forecasting model); novel intelligent methodologies (chaos theory; cloud theory; quantum theory)
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Dear Colleagues,

The development of kernel methods and hybrid evolutionary algorithms (HEA) to support experts in business forecasting is of great importance to improve the accuracy of the actions derived from an energy decision maker, and that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required while decisions are made in a competitive environment. Therefore, this is of special relevance in the big data era; these forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking of ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfied parameters in forecasting models. Another issue to be addressed is that of seasonality or cyclicity of energy data, and the dynamic nonlinearity of the data in demanding process itself.

This Special Issue aims to attract researchers with an interest in the research areas described above. Specifically, we are interested in contributions towards the development of HEAs with kernel methods or with other novel methods (chaos theory, fuzzy theory, cloud theory, quantum behavior, and so on), which, with superior capabilities over the traditional optimization approaches, aims to overcome some endogenous drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy. As an example, genetic algorithms with simulated annealing algorithms (GA-SA), by applying the superior capability of SA algorithm to reach more ideal solutions, and by employing the mutation process of GA to enhance the searching process. The new hybrid evolutionary algorithms require more detailed research and empirical studies. On the other hand, some other new trials, namely combined approaches, such as seasonal mechanism or multiple seasonal mechanism that are combined with forecasting models, are also welcome.

All submissions should be based on the rigorous motivation of the mentioned approaches, and all the developed models should also have a corresponding theoretical sound framework, lacking such a scientific approach is discouraged. Validation support of existing/presented approaches is encouraged to be done using real practical applications.

Prof. Dr. Wei-Chiang Hong
Guest Editor

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Keywords

Kernel methods
Evolutionary algorithms
Energy forecasting
Support vector regression
Chaos theory
Fuzzy theory
Cloud theory
Quantum theory

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Published Papers (10 papers)

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9042 KiB

Open AccessArticle

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

by Yuqi Dong, Xuejiao Ma, Chenchen Ma and Jianzhou Wang

Energies 2016, 9(12), 1050; https://doi.org/10.3390/en9121050 - 14 Dec 2016

Cited by 23 | Viewed by 4699

Abstract

Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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1335 KiB

Open AccessArticle

Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

by Taiyong Li, Min Zhou, Chaoqi Guo, Min Luo, Jiang Wu, Fan Pan, Quanyi Tao and Ting He

Energies 2016, 9(12), 1014; https://doi.org/10.3390/en9121014 - 1 Dec 2016

Cited by 50 | Viewed by 6843

Abstract

Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD), adaptive particle swarm optimization (APSO), and relevance vector machine (RVM)—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs) and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI) to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE), mean absolute percent error (MAPE), and directional statistic (Dstat), showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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2462 KiB

Open AccessArticle

Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

by Yi Liang, Dongxiao Niu, Ye Cao and Wei-Chiang Hong

Energies 2016, 9(11), 941; https://doi.org/10.3390/en9110941 - 11 Nov 2016

Cited by 21 | Viewed by 5547

Abstract

The power industry is the main battlefield of CO₂ emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD) with induced ordered weighted harmonic averaging operator (IOWHA) to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM) forecasting model and multiple regression (MR) model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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1706 KiB

Open AccessArticle

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

by Cheng-Wen Lee and Bing-Yi Lin

Energies 2016, 9(11), 873; https://doi.org/10.3390/en9110873 - 26 Oct 2016

Cited by 25 | Viewed by 5260

Abstract

Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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3268 KiB

Open AccessArticle

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

by Yi Liang, Dongxiao Niu, Minquan Ye and Wei-Chiang Hong

Energies 2016, 9(10), 827; https://doi.org/10.3390/en9100827 - 17 Oct 2016

Cited by 34 | Viewed by 5627 | Correction

Abstract

Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT) and least squares support vector machine (LSSVM), which is optimized by an improved cuckoo search (CS). To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day’s load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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1377 KiB

Open AccessArticle

Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting

by Min-Liang Huang

Energies 2016, 9(6), 426; https://doi.org/10.3390/en9060426 - 31 May 2016

Cited by 28 | Viewed by 5223

Abstract

In existing forecasting research papers support vector regression with chaotic mapping function and evolutionary algorithms have shown their advantages in terms of forecasting accuracy improvement. However, for classical particle swarm optimization (PSO) algorithms, trapping in local optima results in an earlier standstill of the particles and lost activities, thus, its core drawback is that eventually it produces low forecasting accuracy. To continue exploring possible improvements of the PSO algorithm, such as expanding the search space, this paper applies quantum mechanics to empower each particle to possess quantum behavior, to enlarge its search space, then, to improve the forecasting accuracy. This investigation presents a support vector regression (SVR)-based load forecasting model which hybridizes the chaotic mapping function and quantum particle swarm optimization algorithm with a support vector regression model, namely the SVRCQPSO (support vector regression with chaotic quantum particle swarm optimization) model, to achieve more accurate forecasting performance. Experimental results indicate that the proposed SVRCQPSO model achieves more accurate forecasting results than other alternatives. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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4108 KiB

Open AccessArticle

Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting

by Li-Ling Peng, Guo-Feng Fan, Min-Liang Huang and Wei-Chiang Hong

Energies 2016, 9(3), 221; https://doi.org/10.3390/en9030221 - 19 Mar 2016

Cited by 42 | Viewed by 6697

Abstract

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD) method and quantum particle swarm optimization algorithm (QPSO) for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF) and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia) market and the New York Independent System Operator (NYISO, New York, USA) are used for comparing the forecasting performances of different forecasting models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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514 KiB

Open AccessArticle

Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

by Yan Hong Chen, Wei-Chiang Hong, Wen Shen and Ning Ning Huang

Energies 2016, 9(2), 70; https://doi.org/10.3390/en9020070 - 26 Jan 2016

Cited by 92 | Viewed by 7624

Abstract

This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS) and global harmony search algorithm (GHSA) with least squares support vector machines (LSSVM), namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS) algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA) model and other algorithms hybridized with LSSVM including genetic algorithm (GA), particle swarm optimization (PSO), harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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2659 KiB

Open AccessArticle

A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

by Guoqiang Sun, Tong Chen, Zhinong Wei, Yonghui Sun, Haixiang Zang and Sheng Chen

Energies 2016, 9(1), 54; https://doi.org/10.3390/en9010054 - 19 Jan 2016

Cited by 96 | Viewed by 8551

Abstract

Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on variational mode decomposition (VMD) and spiking neural networks (SNNs) is proposed. An original carbon price series is firstly decomposed into a series of relatively stable components through VMD to simplify the interference and coupling across characteristic information of different scales in the data. Then, a SNN forecasting model is built for each component, and the partial autocorrelation function (PACF) is used to determine the input variables for each SNN model. The final forecasting result for the original carbon price can be obtained by aggregating the forecasting results of all the components. Actual InterContinental Exchange (ICE) carbon price data is used for simulation, and comprehensive evaluation criteria are proposed for quantitative error evaluation. Simulation results and analysis suggest that the proposed VMD-SNN forecasting model outperforms conventional models in terms of forecasting accuracy and reliability. Full article

(This article belongs to the Special Issue Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting)

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138 KiB

Open AccessCorrection

Correction: Liang, Y., et al. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search. Energies 2016, 9, 827

by Yi Liang, Dongxiao Niu, Minquan Ye and Wei-Chiang Hong

Energies 2016, 9(12), 1076; https://doi.org/10.3390/en9121076 - 16 Dec 2016

Cited by 1 | Viewed by 3092

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