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Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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

Deadline for manuscript submissions: closed (31 December 2017) | Viewed by 54524

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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,

More accurate, or more precise, energy demand forecasts is required while energy decisions are made in a competitive environment. Particularly, in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated; examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgments and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and evolutionary computation techniques can provide important improvements via well parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.

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 any hybrid advanced optimization methods (e.g., quadratic (nonlinear) programming theory, chaos theory, fuzzy theory, cloud theory, quantum theory, differential empirical mode, and so on) with evolutionary computation techniques (e.g., genetic algorithms, evolutionary algorithms, ant colony algorithm, immune algorithm, bacterial foraging algorithm, swarm intelligence, and so on), which have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and, then, apply these advanced hybrid approaches to enhance the capabilities of original forecasting models to significantly improve forecasting accuracy. For example, the hybrid cloud theory with the simulated annealing algorithm (CSA), by introducing a cloud generator to the temperature annealing process, can randomly generate a group of new values that distribute around the given value like a “cloud”. The fixed temperature point of each step can be transformed into a changeable temperature zone, in which the temperature of each state generated at every annealing step can be randomly chosen, the course of temperature change in the entire annealing process is assumed to be continuous, which is the required condition of a physical annealing process. Eventually, the hybrid approach can reach more ideal solutions. These kind of hybrid approaches require more detailed research and empirical studies. On the other hand, some new trials, namely combined approaches, such as single seasonal mechanism, multiple seasonal mechanism, longitudinal seasonal mechanism, and cross-sectional seasonal mechanism, etc., combined with forecasting models, are also welcome.

All submissions should be based on the rigorous motivation of the mentioned approaches and all developed models should also have corresponding sound theoretical framework, not having such a scientific approach is discouraged. Validation 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

Hybrid models
Optimization methods
Evolutionary algorithms
Energy forecasting
Support vector regression
Chaos theory
Fuzzy theory
Cloud theory
Quantum theory
Single seasonal mechanism
Multiple seasonal mechanism
Longitudinal seasonal mechanism
Cross-sectional seasonal mechanism

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Published Papers (11 papers)

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Research

23 pages, 10453 KiB

Open AccessArticle

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

by Peng Lu, Lin Ye, Bohao Sun, Cihang Zhang, Yongning Zhao and Jingzhu Teng

Energies 2018, 11(4), 697; https://doi.org/10.3390/en11040697 - 21 Mar 2018

Cited by 52 | Viewed by 4931

Abstract

Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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4970 KiB

Open AccessArticle

Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting

by Ming-Wei Li, Jing Geng, Shumei Wang and Wei-Chiang Hong

Energies 2017, 10(12), 2180; https://doi.org/10.3390/en10122180 - 19 Dec 2017

Cited by 37 | Viewed by 5071

Abstract

Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat’s search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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5967 KiB

Open AccessArticle

The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction

by Dongxiao Niu, Haichao Wang, Hanyu Chen and Yi Liang

Energies 2017, 10(12), 2066; https://doi.org/10.3390/en10122066 - 5 Dec 2017

Cited by 35 | Viewed by 5090

Abstract

Accurate and stable prediction of icing thickness on transmission lines is of great significance for ensuring the safe operation of the power grid. In order to improve the accuracy and stability of icing prediction, an innovative prediction model based on the generalized regression neural network (GRNN) and the fruit fly optimization algorithm (FOA) is proposed. Firstly, a feature selection method based on the data inconsistency rate (IR) is adopted to select the optimal feature, which aims to reduce redundant input vectors. Then, the fruit FOA is utilized for optimization of smoothing factor for the GRNN. Lastly, the icing forecasting method FOA-IR-GRNN is established. Two cases in different locations and different months are selected to validate the proposed model. The results indicate that the new hybrid FOA-IR-GRNN model presents better accuracy, robustness, and generality in icing forecasting. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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4812 KiB

Open AccessArticle

Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA

by Dongxiao Niu, Yi Liang and Wei-Chiang Hong

Energies 2017, 10(12), 2001; https://doi.org/10.3390/en10122001 - 1 Dec 2017

Cited by 47 | Viewed by 4735

Abstract

As a kind of clean and renewable energy, wind power is winning more and more attention across the world. Regarding wind power utilization, safety is a core concern and such concern has led to many studies on predicting wind speed. To obtain a more accurate prediction of the wind speed, this paper adopts a new hybrid forecasting model, combing empirical mode decomposition (EMD) and the general regression neural network (GRNN) optimized by the fruit fly optimization algorithm (FOA). In this new model, the original wind speed series are first decomposed into a collection of intrinsic mode functions (IMFs) and a residue. Next, the inherent relationship (partial correlation) of the datasets is analyzed, and the results are then used to select the input for the forecasting model. Finally, the GRNN with the FOA to optimize the smoothing factor is used to predict each sub-series. The mean absolute percentage error of the forecasting results in two cases are respectively 8.95% and 9.87%, suggesting that the hybrid approach outperforms the compared models, which provides guidance for future wind speed forecasting. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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2569 KiB

Open AccessArticle

Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search

by Xiaomin Xu, Dongxiao Niu, Lihui Zhang, Yongli Wang and Keke Wang

Energies 2017, 10(11), 1862; https://doi.org/10.3390/en10111862 - 14 Nov 2017

Cited by 17 | Viewed by 4113

Abstract

With the increase in energy demand, extreme climates have gained increasing attention. Ice disasters on transmission lines can cause gap discharge and icing flashover electrical failures, which can lead to mechanical failure of the tower, conductor, and insulators, causing significant harm to people’s daily life and work. To address this challenge, an intelligent combinational model is proposed based on improved empirical mode decomposition and support vector machine for short-term forecasting of ice cover thickness. Firstly, in light of the characteristics of ice cover thickness data, fast independent component analysis (FICA) is implemented to smooth the abnormal situation on the curve trend of the original data for prediction. Secondly, ensemble empirical mode decomposition (EEMD) decomposes data after denoising it into different components from high frequency to low frequency, and support vector machine (SVM) is introduced to predict the sequence of different components. Then, some modifications are performed on the standard SVM algorithm to accelerate the convergence speed. Combined with the advantages of genetic algorithm and tabu search, the combination algorithm is introduced to optimize the parameters of support vector machine. To improve the prediction accuracy, the kernel function of the support vector machine is adaptively adopted according to the complexity of different sequences. Finally, prediction results for each component series are added to obtain the overall ice cover thickness. A 220 kV DC transmission line in the Hunan Region is taken as the case study to verify the practicability and effectiveness of the proposed method. Meanwhile, we select SVM optimized by genetic algorithm (GA-SVM) and traditional SVM algorithm for comparison, and use the error function of mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) to compare prediction accuracy. Finally, we find that these improvements facilitate the forecasting efficiency and improve the performance of the model. As a result, the proposed model obtains more ideal solutions and has higher accuracy and stronger generalization than other algorithms. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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2622 KiB

Open AccessArticle

Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting

by Cheng-Wen Lee and Bing-Yi Lin

Energies 2017, 10(11), 1832; https://doi.org/10.3390/en10111832 - 10 Nov 2017

Cited by 17 | Viewed by 4888

Abstract

Accurate electricity forecasting is still the critical issue in many energy management fields. The applications of hybrid novel algorithms with support vector regression (SVR) models to overcome the premature convergence problem and improve forecasting accuracy levels also deserve to be widely explored. This paper applies chaotic function and quantum computing concepts to address the embedded drawbacks including crossover and mutation operations of genetic algorithms. Then, this paper proposes a novel electricity load forecasting model by hybridizing chaotic function and quantum computing with GA in an SVR model (named SVRCQGA) to achieve more satisfactory forecasting accuracy levels. Experimental examples demonstrate that the proposed SVRCQGA model is superior to other competitive models. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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5121 KiB

Open AccessArticle

Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model

by Guo-Feng Fan, Li-Ling Peng, Xiangjun Zhao and Wei-Chiang Hong

Energies 2017, 10(11), 1713; https://doi.org/10.3390/en10111713 - 26 Oct 2017

Cited by 53 | Viewed by 4758

Abstract

Providing accurate load forecasting plays an important role for effective management operations of a power utility. When considering the superiority of support vector regression (SVR) in terms of non-linear optimization, this paper proposes a novel SVR-based load forecasting model, namely EMD-PSO-GA-SVR, by hybridizing the empirical mode decomposition (EMD) with two evolutionary algorithms, i.e., particle swarm optimization (PSO) and the genetic algorithm (GA). The EMD approach is applied to decompose the load data pattern into sequent elements, with higher and lower frequencies. The PSO, with global optimizing ability, is employed to determine the three parameters of a SVR model with higher frequencies. On the contrary, for lower frequencies, the GA, which is based on evolutionary rules of selection and crossover, is used to select suitable values of the three parameters. Finally, the load data collected from the New York Independent System Operator (NYISO) in the United States of America (USA) and the New South Wales (NSW) in the Australian electricity market are used to construct the proposed model and to compare the performances among different competitive forecasting models. The experimental results demonstrate the superiority of the proposed model that it can provide more accurate forecasting results and the interpretability than others. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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677 KiB

Open AccessArticle

Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

by Nadeem Javaid, Fahim Ahmed, Ibrar Ullah, Samia Abid, Wadood Abdul, Atif Alamri and Ahmad S. Almogren

Energies 2017, 10(10), 1546; https://doi.org/10.3390/en10101546 - 8 Oct 2017

Cited by 58 | Viewed by 5687

Abstract

In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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2322 KiB

Open AccessArticle

Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method

by Dongxiao Niu, Yi Liang, Haichao Wang, Meng Wang and Wei-Chiang Hong

Energies 2017, 10(8), 1196; https://doi.org/10.3390/en10081196 - 13 Aug 2017

Cited by 21 | Viewed by 5153

Abstract

Stable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) based on the variance-covariance (VC) weight determination method. Firstly, the initial weights and thresholds of BPNN are optimized by mind evolutionary computation (MEC) to prevent the BPNN from falling into local optima and speed up its convergence. Secondly, a bat algorithm (BA) is utilized to optimize the key parameters of SVM. Thirdly, the kernel function is introduced into an extreme learning machine (ELM) to improve the regression prediction accuracy of the model. Lastly, after adopting the above three modified models to predict, the variance-covariance weight determination method is applied to combine the forecasting results. Through performance verification of the model by real-world examples, the results show that the forecasting accuracy of the three individual modified models proposed in this paper has been improved, but the stability is poor, whereas the combination forecasting method proposed in this paper is not only accurate, but also stable. As a result, it can provide technical reference for the safety management of power grid. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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4779 KiB

Open AccessArticle

An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting

by Ping Jiang, Zeng Wang, Kequan Zhang and Wendong Yang

Energies 2017, 10(7), 954; https://doi.org/10.3390/en10070954 - 9 Jul 2017

Cited by 3 | Viewed by 4778

Abstract

Wind speed forecasting has an unsuperseded function in the high-efficiency operation of wind farms, and is significant in wind-related engineering studies. Back-propagation (BP) algorithms have been comprehensively employed to forecast time series that are nonlinear, irregular, and unstable. However, the single model usually overlooks the importance of data pre-processing and parameter optimization of the model, which results in weak forecasting performance. In this paper, a more precise and robust model that combines data pre-processing, BP neural network, and a modified artificial intelligence optimization algorithm was proposed, which succeeded in avoiding the limitations of the individual algorithm. The novel model not only improves the forecasting accuracy but also retains the advantages of the firefly algorithm (FA) and overcomes the disadvantage of the FA while optimizing in the later stage. To verify the forecasting performance of the presented hybrid model, 10-min wind speed data from Penglai city, Shandong province, China, were analyzed in this study. The simulations revealed that the proposed hybrid model significantly outperforms other single metaheuristics. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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3920 KiB

Open AccessArticle

A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer

by Sen Guo, Haoran Zhao and Huiru Zhao

Energies 2017, 10(7), 922; https://doi.org/10.3390/en10070922 - 4 Jul 2017

Cited by 11 | Viewed by 4305

Abstract

As one of the most promising kinds of the renewable energy power, wind power has developed rapidly in recent years. However, wind power has the characteristics of intermittency and volatility, so its penetration into electric power systems brings challenges for their safe and stable operation, therefore making accurate wind power forecasting increasingly important, which is also a challenging task. In this paper, a new hybrid wind power forecasting method, named the BND-ALO-RVM forecaster, is proposed. It combines the Beveridge-Nelson decomposition method (BND), relevance vector machine (RVM) and ant lion optimizer (ALO). Considering the nonlinear and non-stationary characteristics of wind power data, the wind power time series were firstly decomposed into deterministic, cyclical and stochastic components using BND. Then, these three decomposed components were respectively forecasted using RVM. Meanwhile, to improve the forecasting performance, the kernel width parameter of RVM was optimally determined by ALO, a new Nature-inspired meta-heuristic algorithm. Finally, the wind power forecasting result was obtained by multiplying the forecasting results of those three components. The proposed BND-ALO-RVM wind power forecaster was tested with real-world hourly wind power data from the Xinjiang Uygur autonomous region in China. To verify the effectiveness and feasibility of the proposed forecaster, it was compared with single RVM without time series decomposition and parameter optimization, RVM with time series decomposition based on BND (BND-RVM), RVM with parameter optimization (ALO-RVM), and Generalized Regression Neural Network with data decomposition based on Wavelet Transform (WT-GRNN) using three forecasting performance criteria, namely MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error). The results indicate the proposed BND-ALO-RVM wind power forecaster has the best forecasting performance of all the tested options, which confirms its validity. Full article

(This article belongs to the Special Issue Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting)

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