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Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112412014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), kernel extreme learning machine (KELM) and fireworks algorithm (FWA) is proposed. First, KPCA modal is used to reduce the dimension of the feature, thus redundant input samples are merged. Next, FWA is employed to optimize the parameters C and σ of KELM. Lastly, the load forecasting modal of KPCA-FWA-KELM is established. The relevant data of a distributed energy system in Beijing, China, is selected for training test to verify the effectiveness of the proposed method. The results show that the new hybrid KPCA-FWA-KELM method has superior performance, robustness and versatility in load prediction of distributed energy systems.
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Aasim, Singh S, Mohapatra A. Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107730] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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A Stochastic Estimation Framework for Yearly Evolution of Worldwide Electricity Consumption. FORECASTING 2021. [DOI: 10.3390/forecast3020016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The determination of electric energy consumption is remarked as one of the most vital objectives for electrical engineers as it is highly essential in determining the actual energy demand made on the existing electricity supply. Therefore, it is important to find out about the increasing trend in electric energy demands and use all over the world. In this work, we present a prediction scheme for the progression of worldwide aggregates of cumulative electricity consumption using the time series of the records released annually for the net electricity use throughout the world. Consequently, we make use of an autoregressive (AR) model by retaining the best possible autoregression order recording the highest regression accuracy and the lowest standardized regression error. The resultant regression scheme was proficiently employed to regress and forecast the evolution of next-decade data for the net consumption of electricity worldwide from 1980 to 2019 (in billion kilowatt-hours). The experimental outcomes exhibited that the highest accuracy in regressing and forecasting the global consumption of electricity is 95.7%. The prediction results disclose a linearly growing trend in the amount of electricity issued annually over the past four decades’ observation for the global net electricity consumption dataset.
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Abstract
The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study.
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Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models. ENERGIES 2020. [DOI: 10.3390/en13061407] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios.
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Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors. ENERGIES 2019. [DOI: 10.3390/en12193603] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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.
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Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index. ELECTRONICS 2018. [DOI: 10.3390/electronics7120431] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.
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Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron. ENERGIES 2018. [DOI: 10.3390/en11123283] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
A stable power supply is very important in the management of power infrastructure. One of the critical tasks in accomplishing this is to predict power consumption accurately, which usually requires considering diverse factors, including environmental, social, and spatial-temporal factors. Depending on the prediction scope, building type can also be an important factor since the same types of buildings show similar power consumption patterns. A university campus usually consists of several building types, including a laboratory, administrative office, lecture room, and dormitory. Depending on the temporal and external conditions, they tend to show a wide variation in the electrical load pattern. This paper proposes a hybrid short-term load forecast model for an educational building complex by using random forest and multilayer perceptron. To construct this model, we collect electrical load data of six years from a university campus and split them into training, validation, and test sets. For the training set, we classify the data using a decision tree with input parameters including date, day of the week, holiday, and academic year. In addition, we consider various configurations for random forest and multilayer perceptron and evaluate their prediction performance using the validation set to determine the optimal configuration. Then, we construct a hybrid short-term load forecast model by combining the two models and predict the daily electrical load for the test set. Through various experiments, we show that our hybrid forecast model performs better than other popular single forecast models.
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Deng S, Yuan C, Yang L, Zhang L. Distributed electricity load forecasting model mining based on hybrid gene expression programming and cloud computing. Pattern Recognit Lett 2018. [DOI: 10.1016/j.patrec.2017.10.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model. ENERGIES 2018. [DOI: 10.3390/en11061605] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8060864] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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14
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Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks. ENERGIES 2018. [DOI: 10.3390/en11051289] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Qiu X, Suganthan PN, Amaratunga GA. Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.01.015] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions. ENERGIES 2018. [DOI: 10.3390/en11030596] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Chen Y, Yu H, Cheng Y, Cheng Q, Li D. A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture. PLoS One 2018; 13:e0192456. [PMID: 29466394 PMCID: PMC5821340 DOI: 10.1371/journal.pone.0192456] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 01/23/2018] [Indexed: 11/18/2022] Open
Abstract
A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.
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Affiliation(s)
- Yingyi Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
| | - Huihui Yu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
| | - Yanjun Cheng
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
| | - Qianqian Cheng
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
| | - Daoliang Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, P.R. China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, P.R. China
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Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System. ENERGIES 2017. [DOI: 10.3390/en10091303] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The introduction of renewable resources into the distributed energy system has challenged the operation optimization of the distributed energy system. Integration of new technologies and diversified characteristics on the demand side has exerted a great influence on the distributed energy system. In this paper, by way of literature review, first, the topological structure and the mathematical expression of the distributed energy system were summarized, and the trend of enrichment and diversification and the new characteristics of the system were evaluated. Then, the load forecasting technology was reviewed and analyzed from two aspects, fundamental research and application research. Research methods of the distributed energy system under the new trend of energies were discussed, and the boundaries of the broadened distributed energy technology were explored.
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Shabanian SR, Abdoos AA. A hybrid soft computing approach based on feature selection for estimation of filtration combustion characteristics. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2956-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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20
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Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search. ENERGIES 2016. [DOI: 10.3390/en9100827] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Dissolved oxygen content prediction in crab culture using a hybrid intelligent method. Sci Rep 2016; 6:27292. [PMID: 27270206 PMCID: PMC4897606 DOI: 10.1038/srep27292] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 05/17/2016] [Indexed: 11/15/2022] Open
Abstract
A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds.
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