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Intelligent Optimization Modelling in Energy Forecasting

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 40742

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Special Issue Editor

Prof. Dr. Wei-Chiang Hong

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Guest Editor

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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Special Issue Information

Dear Colleagues,

Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In the past decades, many energy forecasting models have been continuously proposed to improve the forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.).

Recently, due to the great development of optimization modelling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is worthwhile to explore the tendency and development of intelligent-optimization-based modelling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

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

Prof. Dr. Wei-Chiang Hong
Guest Editor

Manuscript Submission Information

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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 semimonthly 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 2600 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

statistical forecasting models (ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, etc.)
artificial neural network (ANNs) models
knowledge-based expert system models
fuzzy theory and fuzzy inference system models
evolutionary computation models
support vector regression (SVR) models
hybrid models
combined models
evolutionary algorithms
meta-heuristic algorithms
seasonal mechanisms (single seasonal mechanism
multiple seasonal mechanism)
intelligent computing mechanisms (chaotic mapping mechanism
quantum computing mechanism
cloud mapping mechanism)
marine renewable energy forecasting
electric load forecasting
energy forecasting.

Published Papers (11 papers)

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Research

Jump to: Review

20 pages, 1014 KiB

Open AccessArticle

Ensemble of Regression-Type and Interpolation-Type Metamodels

by Cheng Yan, Jianfeng Zhu, Xiuli Shen, Jun Fan, Dong Mi and Zhengming Qian

Energies 2020, 13(3), 654; https://0-doi-org.brum.beds.ac.uk/10.3390/en13030654 - 04 Feb 2020

Cited by 4 | Viewed by 2163

Abstract

Metamodels have become increasingly popular in the field of energy sources because of their significant advantages in reducing the computational cost of time-consuming tasks. Lacking the prior knowledge of actual physical systems, it may be difficult to find an appropriate metamodel in advance for a new task. A favorite way of overcoming this difficulty is to construct an ensemble metamodel by assembling two or more individual metamodels. Motivated by the existing works, a novel metamodeling approach for building the ensemble metamodels is proposed in this paper. By thoroughly exploring the characteristics of regression-type and interpolation-type metamodels, some useful information is extracted from the feedback of the regression-type metamodels to further improve the functional fitting capability of the ensemble metamodels. Four types of ensemble metamodels were constructed by choosing four individual metamodels. Common benchmark problems are chosen to compare the performance of the individual and ensemble metamodels. The results show that the proposed metamodeling approach reduces the risk of selecting the worst individual metamodel and improves the accuracy of the used individual metamodels. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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24 pages, 1598 KiB

Open AccessArticle

A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading

by Oscar V. De la Torre-Torres, Evaristo Galeana-Figueroa and José Álvarez-García

Energies 2020, 13(1), 129; https://0-doi-org.brum.beds.ac.uk/10.3390/en13010129 - 26 Dec 2019

Cited by 15 | Viewed by 3602

Abstract

In this paper, we test the use of Markov-switching (MS) GARCH (MSGARCH) models for trading either oil or natural gas futures. Using weekly data from 7 January 1994 to 31 May 2019, we tested the next trading rule: to invest in the simulated commodity if the investor expects to be in the low-volatility regime at t + 1 or to otherwise hold the risk-free asset. Assumptions for our simulations included the following: (1) we assumed that the investors trade in a homogeneous (Gaussian or t-Student) two regime context and (2) the investor used a time-fixed, ARCH, or GARCH variance in each regime. Our results suggest that the use of the MS Gaussian model, with time-fixed variance, leads to the best performance in the oil market. For the case of natural gas, we found no benefit of using our trading rule against a buy-and-hold strategy in the three-month U.S. Treasury bills. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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17 pages, 1934 KiB

Open AccessArticle

A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression

by Yuansheng Huang, Lei Yang, Chong Gao, Yuqing Jiang and Yulin Dong

Energies 2019, 12(21), 4181; https://0-doi-org.brum.beds.ac.uk/10.3390/en12214181 - 01 Nov 2019

Cited by 8 | Viewed by 2609

Abstract

Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation. Construction of an accurate and reliable prediction model for the volatility changes in energy consumption can provide valuable reference information for policy makers of the government and for the energy industry. In view of this, a novel improved model is developed in this article by integrating the modified state transition algorithm (MSTA) with the Gaussian processes regression (GPR) approach for non-fossil energy consumption predictions for China at the end of the 13th Five-Year Project, in which the MSTA is utilized for effective optimization of hyper-parameters in GPR. Aiming for validating the superiority of MSTA, several comparisons are conducted on two well-known functions and the optimization results show the effectiveness of modification in the state transition algorithm (STA). Then, based on the latest statistical renewable energy consumption data, the MSTA-GPR model is utilized to generate consumption predictions for overall renewable energy and each single renewable energy source, including hydropower, wind, solar, geothermal, biomass and other energies, respectively. The forecasting results reveal that the proposed improved GPR can promote the forecasting ability of basic GPR and obtain the best prediction effect among all the other comparison models. Finally, combined with the forecasting results, the trend of each renewable energy source is analyzed. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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25 pages, 2905 KiB

Open AccessArticle

Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

by Taiyong Li, Yingrui Zhou, Xinsheng Li, Jiang Wu and Ting He

Energies 2019, 12(19), 3603; https://0-doi-org.brum.beds.ac.uk/10.3390/en12193603 - 20 Sep 2019

Cited by 33 | Viewed by 3712

Abstract

As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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38 pages, 9704 KiB

Open AccessArticle

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting

by Danxiang Wei, Jianzhou Wang, Kailai Ni and Guangyu Tang

Energies 2019, 12(18), 3588; https://0-doi-org.brum.beds.ac.uk/10.3390/en12183588 - 19 Sep 2019

Cited by 21 | Viewed by 3483

Abstract

In recent years, although deep learning algorithms have been widely applied to various fields, ranging from translation to time series forecasting, researchers paid limited attention to modelling parameter optimization and the combination of the fuzzy time series. In this paper, a novel hybrid forecasting system, named CFML (complementary ensemble empirical mode decomposition (CEEMD)-fuzzy time series (FTS)-multi-objective grey wolf optimizer (MOGWO)-long short-term memory (LSTM)), is proposed and tested. This model is based on the LSTM model with parameters optimized by MOGWO, before which a fuzzy time series method involving the LEM2 (learning from examples module version two) algorithm is adopted to generate the final input data of the optimized LSTM model. In addition, the CEEMD algorithm is also used to de-noise and decompose the raw data. The CFML model successfully overcomes the nonstationary and irregular features of wind speed data and electrical power load series. Several experimental results covering four wind speed datasets and two electrical power load datasets indicate that our hybrid forecasting system achieves average improvements of 49% and 70% in wind speed and electrical power load, respectively, under the metric MAPE (mean absolute percentage error). Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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21 pages, 2404 KiB

Open AccessArticle

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

by Hongwei Wang, Yuansheng Huang, Chong Gao and Yuqing Jiang

Energies 2019, 12(16), 3043; https://0-doi-org.brum.beds.ac.uk/10.3390/en12163043 - 07 Aug 2019

Cited by 2 | Viewed by 2605

Abstract

Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit fly optimization algorithm (MFOA) and a deep convolutional neural network (DCNN). Firstly, the DIR integrated with the MFOA is adopted for input feature selection. Simultaneously, the MFOA is utilized to realize parameter optimization in the DCNN. The effectiveness of the MFOA–DIR–DCNN has been validated by a case study that selects 128 substation projects in different regions for training and testing. The modeling results demonstrate that this established approach is better than the contrast methods with regard to forecasting accuracy and robustness. Thus, the developed technique is feasible for the cost prediction of substation projects in various voltage levels. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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22 pages, 3740 KiB

Open AccessArticle

Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy

by Yuansheng Huang, Lei Yang, Shijian Liu and Guangli Wang

Energies 2019, 12(10), 1822; https://0-doi-org.brum.beds.ac.uk/10.3390/en12101822 - 14 May 2019

Cited by 32 | Viewed by 3728

Abstract

It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models were applied on three different wind speed sequences. The calculation outcomes reveal that: (1) the EEMD is able to boost the wind speed prediction capacity and robustness of the LSTM approach effectively; (2) the BSO based parameter optimization method is effective in finding the optimal parameter for GRNN and improving the forecasting performance for the EEMD-LSTM-GRNN model; (3) the error correction method based on the optimized GRNN promotes the forecasting accuracy of the EEMD-LSTM model significantly; and (4) compared with all models involved, the proposed EEMD-LSTM-GRNN-BSO model is proved to have the best performance in predicting the short-term wind speed sequence. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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23 pages, 2064 KiB

Open AccessArticle

An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting

by Jiang Wu, Yu Chen, Tengfei Zhou and Taiyong Li

Energies 2019, 12(7), 1239; https://0-doi-org.brum.beds.ac.uk/10.3390/en12071239 - 01 Apr 2019

Cited by 39 | Viewed by 3824

Abstract

Crude oil is one of the main energy sources and its prices have gained increasing attention due to its important role in the world economy. Accurate prediction of crude oil prices is an important issue not only for ordinary investors, but also for the whole society. To achieve the accurate prediction of nonstationary and nonlinear crude oil price time series, an adaptive hybrid ensemble learning paradigm integrating complementary ensemble empirical mode decomposition (CEEMD), autoregressive integrated moving average (ARIMA) and sparse Bayesian learning (SBL), namely CEEMD-ARIMA&SBL-SBL (CEEMD-A&S-SBL), is developed in this study. Firstly, the decomposition method CEEMD, which can reduce the end effects and mode mixing, was employed to decompose the original crude oil price time series into intrinsic mode functions (IMFs) and one residue. Then, ARIMA and SBL with combined kernels were applied to predict target values for the residue and each single IMF independently. Finally, the predicted values of the above two models for each component were adaptively selected based on the training precision, and then aggregated as the final forecasting results using SBL without kernel-tricks. Experiments were conducted on the crude oil spot prices of the West Texas Intermediate (WTI) and Brent crude oil to evaluate the performance of the proposed CEEMD-A&S-SBL. The experimental results demonstrated that, compared with some state-of-the-art prediction models, CEEMD-A&S-SBL can significantly improve the prediction accuracy of crude oil prices in terms of the root mean squared error (RMSE), the mean absolute percent error (MAPE), and the directional statistic (Dstat). Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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16 pages, 4014 KiB

Open AccessArticle

Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

by Wei-Chiang Hong and Guo-Feng Fan

Energies 2019, 12(6), 1093; https://0-doi-org.brum.beds.ac.uk/10.3390/en12061093 - 21 Mar 2019

Cited by 54 | Viewed by 3481

Abstract

For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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19 pages, 3909 KiB

Open AccessArticle

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

by Guo-Feng Fan, Yan-Hui Guo, Jia-Mei Zheng and Wei-Chiang Hong

Energies 2019, 12(5), 916; https://0-doi-org.brum.beds.ac.uk/10.3390/en12050916 - 09 Mar 2019

Cited by 98 | Viewed by 7457

Abstract

In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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Review

Jump to: Research

18 pages, 1536 KiB

Open AccessReview

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

by Jesús Ferrero Bermejo, Juan Francisco Gómez Fernández, Rafael Pino, Adolfo Crespo Márquez and Antonio Jesús Guillén López

Energies 2019, 12(21), 4163; https://0-doi-org.brum.beds.ac.uk/10.3390/en12214163 - 31 Oct 2019

Cited by 11 | Viewed by 3097

Abstract

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important effort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the different outputs for the different techniques. Full article

(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)

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