E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting"

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

Deadline for manuscript submissions: 31 December 2017

Special Issue Editor

Guest Editor
Prof. Dr. Wei-Chiang Hong

1. Department of Information Management, Oriental Institute of Technology, No. 58, Sec. 2, Sichuan Rd., Panchiao, Taipei, 220, Taiwan
2. Distinguished Professor at School of Education Intelligent Technology, Jiangsu Normal University, Xuzhou, China
Website | E-Mail
Interests: computational intelligence (neural networks; evolutionary computation); application of forecasting technology (ARIMA; support vector regression; chaos theory)

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Published Papers (3 papers)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

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
Energies 2017, 10(8), 1196; doi:10.3390/en10081196
Received: 26 May 2017 / Revised: 10 August 2017 / Accepted: 11 August 2017 / Published: 13 August 2017
PDF Full-text (2322 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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
Figures

Figure 1

Open AccessArticle An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting
Energies 2017, 10(7), 954; doi:10.3390/en10070954
Received: 22 June 2017 / Revised: 4 July 2017 / Accepted: 4 July 2017 / Published: 9 July 2017
PDF Full-text (4779 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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
Figures

Figure 1

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
Energies 2017, 10(7), 922; doi:10.3390/en10070922
Received: 15 May 2017 / Revised: 11 June 2017 / Accepted: 28 June 2017 / Published: 4 July 2017
PDF Full-text (3920 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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
Figures

Figure 1

Journal Contact

MDPI AG
Energies Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Special Issue Edit a special issue Review for Energies
logo
loading...
Back to Top