Frontier Research in Energy Forecasting

Topic Information

Dear Colleagues,

The modern-day energy sector includes many challenges that affect secure and reliable electricity generation and delivery. Utilities, generation companies, system operators, and other parties have been continually seeking methods to cope with the various challenges. Among the methods, forecasting holds a prominent role in energy systems. Accurate forecasts can limit the technical and economic risks of energy systems operations. Demand forecasting is essential in power generation scheduling, fuel import planning, interconnection operation, and others. Generation forecasting can transform variable energy resources such as photovoltaics and wind turbines into dispatchable sources. Thus, energy forecasting is a scheme with many applications. According to the literature, there is a variety of different energy forecasting models. Time series models refer to autoregressive time functions. These models have historically been the first to be implemented. Recent approaches include machine learning architectures such as neural networks, neuro-fuzzy systems, support vector machines, and others. Machine learning models can effectively simulate nonlinear time series. Finally, a new research trend is deep learning; the scope is to train neural networks in more detail so as to achieve better accuracy. In the context of these challenges, the main scope of this Special Issue is to propose new methods in energy forecasting. State-of-the-art papers together with innovative case studies are invited. Multidisciplinary research and cutting-edge approaches are welcomed in order to address the challenges that are raised by contemporary energy systems and deregulated energy markets.

Dr. Ioannis Panapakidis
Dr. Minas Alexiadis
Dr. Aggelos S. Bouhouras
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600
Forecasting forecasting	3.0	4.0	2019	28.5 Days	CHF 1800
Resources resources	3.3	7.7	2012	23.8 Days	CHF 1600
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (9 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Energies Forecasting Resources Sustainability

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

17 pages, 458 KiB  

Open AccessArticle

Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan

by Hasnain Iftikhar, Nadeela Bibi, Paulo Canas Rodrigues and Javier Linkolk López-Gonzales

Energies 2023, 16(6), 2579; https://doi.org/10.3390/en16062579 - 09 Mar 2023

Cited by 13 | Viewed by 2246

Abstract

In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive [...] Read more.

In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Figure 1

15 pages, 705 KiB  

Open AccessArticle

Ultra Short-Term Power Load Forecasting Based on Similar Day Clustering and Ensemble Empirical Mode Decomposition

by Wenhui Zeng, Jiarui Li, Changchun Sun, Lin Cao, Xiaoping Tang, Shaolong Shu and Junsheng Zheng

Energies 2023, 16(4), 1989; https://doi.org/10.3390/en16041989 - 17 Feb 2023

Cited by 11 | Viewed by 1599

Abstract

With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar day clustering and EEMD (Ensemble Empirical [...] Read more.

With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar day clustering and EEMD (Ensemble Empirical Mode Decomposition). In detail, the K-means clustering algorithm was utilized to divide the historical data into different clusters. Through EEMD, the load data of each cluster were decomposed into several sub-sequences with different time scales. The LSTNet (Long- and Short-term Time-series Network) was adopted as the load forecasting model for these sub-sequences. The forecast results for different sub-sequences were combined as the expected result. The proposed method predicts the load in the next 4 h with an interval of 15 min. The experimental results show that the proposed method obtains higher prediction accuracy than other comparable forecasting models. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Figure 1

17 pages, 3166 KiB  

Open AccessArticle

A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction

by Longnv Huang, Qingyuan Wang, Jiehui Huang, Limin Chen, Yin Liang, Peter X. Liu and Chunquan Li

Energies 2022, 15(13), 4895; https://doi.org/10.3390/en15134895 - 04 Jul 2022

Cited by 1 | Viewed by 1238

Abstract

A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network [...] Read more.

A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network (BTCN). First, ICEEMDAN is introduced to smooth the nonlinear part of the wind speed data by decomposing the raw wind speed data into a series of sequences. Second, SE is applied to quantitatively assess the complexity of each sequence. All sequences are divided into simple sequence set and complex sequence set based on the values of SE. Third, based on the typical broad learning system (BLS), we propose ORBLS with cyclically connected enhancement nodes, which can better capture the dynamic characteristics of the wind. The improved particle swarm optimization (PSO) is used to optimize the hyper-parameters of ORBLS. Fourth, we propose BTCN by adding a dilated causal convolution layer in parallel to each residual block, which can effectively alleviate the local information loss of the temporal convolutional network (TCN) in case of insufficient time series data. Note that ORBLS and BTCN can effectively predict the simple and complex sequences, respectively. To validate the performance of the proposed model, we conducted three predictive experiments on four data sets. The experimental results show that our model obtains the best predictive results on all evaluation metrics, which fully demonstrates the accuracy and robustness of the proposed model. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Figure 1

24 pages, 5517 KiB  

Open AccessArticle

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

by Jun Dong, Xihao Dou, Aruhan Bao, Yaoyu Zhang and Dongran Liu

Sustainability 2022, 14(13), 7767; https://doi.org/10.3390/su14137767 - 25 Jun 2022

Cited by 6 | Viewed by 1842

Abstract

With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information [...] Read more.

With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Figure 1

22 pages, 3382 KiB  

Open AccessArticle

Wind Power Interval Prediction with Adaptive Rolling Error Correction Based on PSR-BLS-QR

by Xu Ran, Chang Xu, Lei Ma and Feifei Xue

Energies 2022, 15(11), 4137; https://doi.org/10.3390/en15114137 - 04 Jun 2022

Cited by 3 | Viewed by 1245

Abstract

Effective prediction of wind power output intervals can capture the trend of uncertain wind output power in the form of probability, which not only can avoid the impact of randomness and volatility on grid security, but also can provide supportable information for grid [...] Read more.

Effective prediction of wind power output intervals can capture the trend of uncertain wind output power in the form of probability, which not only can avoid the impact of randomness and volatility on grid security, but also can provide supportable information for grid dispatching and grid planning. To address the problem of the low accuracy of traditional wind power interval prediction, a new interval prediction method of wind power is proposed based on PSR-BLS-QR with adaptive rolling error correction. First, one-dimensional wind power data are mapped to high-dimensional space by phase space reconstruction (PSR) to achieve data reconstruction and the input and output of the broad learning system (BLS) model are constructed. Second, the training set and the test set are divided according to the input and output data. The BLS model is trained by the training set and the initial power interval of training data is constructed by quantile regression (QR). Then, the error distribution of nonparametric kernel density estimation is constructed at different power interval segments of the interval upper and lower boundaries, respectively, and the corresponding error-corrected power is found. Next, the optimal correction index is used as the objective function to determine the optimal error correction power for different power interval segments of the interval upper and lower boundaries. Finally, a test set is used for testing the performance of the proposed method. Three wind power datasets from different regions are used to prove that the proposed method can improve the average prediction accuracy by about 6–14% with the narrower interval width compared with the traditional interval prediction methods. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Figure 1

17 pages, 723 KiB  

Open AccessArticle

How Does Oil Future Price Imply Bunker Price—Cointegration and Prediction Analysis

by Yanhui Chen, Jinrong Lu and Mengmeng Ma

Energies 2022, 15(10), 3630; https://doi.org/10.3390/en15103630 - 16 May 2022

Cited by 2 | Viewed by 2367

Abstract

This paper investigates how oil’s future price implies the bunker price through cointegration analysis first. A cointegration test confirms the long-run equilibrium condition of bunker and oil future prices. Based on the cointegration relationship, we construct VECM model to forecast bunker prices. In [...] Read more.

This paper investigates how oil’s future price implies the bunker price through cointegration analysis first. A cointegration test confirms the long-run equilibrium condition of bunker and oil future prices. Based on the cointegration relationship, we construct VECM model to forecast bunker prices. In addition, we also consider ARMA, ARMAX, and VAR models for certifying whether considering the long-run equilibrium between bunker and oil future prices is helpful in prediction. One-step-ahead and four-step-ahead forecasting are considered and two out-of-sample datasets are used. The empirical results show that the increase in the value of the error correction term in the VECM model has the effect of pulling down the bunker return. VECM performs better than other models in prediction. The Crude Oil Future Contract 1 has better forecasting performance for bunker prices with VECM in the 1-step-ahead forecast, while Crude Oil Future Contract 3 performs slightly better than Crude Oil Future Contract 1 in the 4-step-ahead forecast. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Figure 1

14 pages, 330 KiB  

Open AccessArticle

Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model

by O-Chia Chuang and Chenxu Yang

Energies 2022, 15(8), 2945; https://doi.org/10.3390/en15082945 - 17 Apr 2022

Cited by 5 | Viewed by 2034

Abstract

Many macro-level variables have been used in forecasting crude oil price volatility. This article aims to identify which variables have the greatest impact and give more accurate predictions. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single [...] Read more.

Many macro-level variables have been used in forecasting crude oil price volatility. This article aims to identify which variables have the greatest impact and give more accurate predictions. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model. By combining the log-likelihood function with adaptive lasso penalty, three most informative determinants have been identified, namely, macroeconomic uncertainty, financial uncertainty and default yield spread. Out-of-sample results show that using these three variables significantly improves prediction accuracy compared to baseline models. However, the variables widely studied by other scholars, such as the supply and demand of crude oil, industrial production index, etc., were not selected, indicating that the impact of these variables may be overestimated. When studying crude oil price volatility, macroeconomic and financial market uncertainties can be used as effective predictors for investors and market analysts. Crude oil market participants should focus on macroeconomic and financial market uncertainties to make risk management more efficient. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Figure 1

20 pages, 5461 KiB  

Open AccessArticle

The Crude Oil International Trade Competition Networks: Evolution Trends and Estimating Potential Competition Links

by Xuanru Zhou, Hua Zhang, Shuxian Zheng, Wanli Xing, Pei Zhao and Haiying Li

Energies 2022, 15(7), 2395; https://doi.org/10.3390/en15072395 - 24 Mar 2022

Cited by 7 | Viewed by 3367

Abstract

In the context of the economic situation, international relations, and the consequences of COVID-19, the future competition pattern of crude oil trade is uncertain. In this paper, the crude oil international import competition and export competition networks are based on a complex network [...] Read more.

In the context of the economic situation, international relations, and the consequences of COVID-19, the future competition pattern of crude oil trade is uncertain. In this paper, the crude oil international import competition and export competition networks are based on a complex network model. The link prediction method is used to construct a crude oil competition relationship prediction model. We summarize the evolving characteristics of the competitive landscape of the global crude oil trade from 2000 to 2019 and explore the reasons for the changes. Finally, we forecast the future potential crude oil import and export competition. The results indicate the following. (1) The crude oil import competition center is transferred from Europe and America to the Asia–Pacific region and it may continue to shift to developing regions. (2) At present, the competition among traditional crude oil exporters is the core of crude oil export competition, such as OPEC, Canada, and Russia. The United States has become the world’s largest crude oil exporter, which means that the core of crude oil export competition has begun to shift to emerging countries. The competition intensity of emerging crude oil exporters is gradually increasing. There is likely to be fierce export competition between traditional and emerging exporters. (3) In the future crude oil competition, we should pay attention to the trend of the United States, which may lead to the restructuring of the global oil trade pattern. Finally, this paper considers the exporters and importers and puts forward policy suggestions for policymakers to deal with the future global crude oil trade competition. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Graphical abstract

14 pages, 2796 KiB  

Open AccessArticle

Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN

by Minfeng Wu and Wen Chen

Sustainability 2022, 14(4), 2206; https://doi.org/10.3390/su14042206 - 15 Feb 2022

Cited by 23 | Viewed by 5087

Abstract

Since electric vehicles (EVs) could reduce the growing concerns on environmental pollution issues and relieve the social dependency of fossil fuels, the EVs market is fast increased in recent years. However, a large growth in the number of EVs will bring a great [...] Read more.

Since electric vehicles (EVs) could reduce the growing concerns on environmental pollution issues and relieve the social dependency of fossil fuels, the EVs market is fast increased in recent years. However, a large growth in the number of EVs will bring a great challenge to the present traffic system; thus, an acceptable model is necessary to forecast the sales of EVs in order to better plan the appropriate supply of necessary facilities (e.g., charging stations and sockets in car parks) as well as the electricity required on the road. In this study, we propose a model to predict the sales volume and increase rate of EVs in the world and China, using both statistics and machine learning methods by combining principle component analysis and a general regression neural network, based on the previous 11 years of sales data of EVs. The results indicate that a continuing growth in the sales of EVs will appear in both the world and China in the coming eight years, but the sales increase rate is slowly and continuously deceasing because of the persistent growth of the basic sales volume. The results also indicate that the increase rate of sales of EVs in China is higher than that of the world, and the proportion of sales of EVs in China will increase gradually and will be above 50% in 2025. In this case, large accessory facilities for EVs are required in China in the coming few years. Full article

(This article belongs to the Topic Frontier Research in Energy Forecasting)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-9