Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Wang, Jidong; Ran, Ran; Song, Zilong; Sun, Jiawen

doi:10.5370/jeet.2017.12.1.064

Cited by 50 publications

(22 citation statements)

References 12 publications

(10 reference statements)

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“…The modeling steps of GWO are as follows: supposing that there are M wolves in a pack and the searching space has k dimensions, the position of grey wolf i can be expressed as x i = (x i1 , x i2 , · · · , x ik ), the behavior of grey wolves encircling the prey can be mathematically expressed using the following equations: where t is the current iteration, x(t) represents the position of wolves at the tth iteration, x p (t) is the position of the prey. The vector b and d can be obtained by Equations (19) and (20).…”

Section: Svmmentioning

confidence: 99%

“…Liu and Huang [18] used simulated annealing algorithm (SA) to optimize the parameters of SVM and proposed a model based on simulated annealing algorithm and support vector machine to forecast the power load that has proven to be of good prediction effect. Wang et al [19] put forward a forecasting model based on environmental factors and support vector machine optimized by genetic algorithm to predict the short-term PV power using the gray correlation coefficient algorithm to find out a similar day of the predicted day. In this paper, the grey wolf optimization algorithm is used to optimize the parameters of support vector machine.…”

Section: Introductionmentioning

confidence: 99%

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Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

2018

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Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm), is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM) is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM ( The experimental results fully demonstrate the reliability and effectiveness of the CEEMDAN-MGWO-SVM model proposed in this paper for daily peak load forecasting, which shows the strong generalization ability and robustness of the model. Keywords: daily peak load forecasting; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); modified grey wolf optimization (MGWO); support vector machine (SVM)

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Section: Svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

2018

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“…It is a class of kernel-based learning methods [33]. Now, the LSSVM has been widely used in forecasting [34][35][36], data fitting [37,38], comprehensive evaluation [39,40] and pattern recognition [41][42][43]. The steps are as follows.…”

Section: The Topsis Improved By the Grey Incidence Analysismentioning

confidence: 99%

Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

Niu

Dai

et al. 2018

Sustainability

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Abstract:The electric power industry is of great significance in promoting social and economic development and improving people's living standards. Power grid construction is a necessary part of infrastructure construction, whose sustainability plays an important role in economic development, environmental protection and social progress. In order to effectively evaluate the sustainability of power grid construction projects, in this paper, we first identified 17 criteria from four dimensions including economy, technology, society and environment to establish the evaluation criteria system. After that, the grey incidence analysis was used to modify the traditional Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), which made it possible to evaluate the sustainability of electric power construction projects based on visual angle of similarity and nearness. Then, in order to simplify the procedure of experts scoring and computation, on the basis of evaluation results of the improved TOPSIS, the model using Modified Fly Optimization Algorithm (MFOA) to optimize the Least Square Support Vector Machine (LSSVM) was established. Finally, a numerical example was given to demonstrate the effectiveness of the proposed model.

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“…K-means clustering [25] and fuzzy c-means [26] are used for clustering of weather types. Self-organizing map (SOM), learning vector quantization (LVQ) [27], gray correlation coefficient [28], generalized weather class (GWC) and SVM [29] methods are effective approaches for weather pattern recognition. In addition, support vector regression (SVR) [27], support vector machines optimized with genetic algorithms (GA-SVM) [28], and particle swarm-optimized SVR (PSO-SVR) [30] can be selected as corresponding regression algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

et al. 2018

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A hybrid photovoltaic (PV) forecasting model is proposed for the ultrashort-term prediction of PV output. The model contains two parts: offline modeling and online forecasting. The offline module uses historical monitoring data to establish a weather type classification model and PV output regression submodels. The online module uses real-time monitoring data for weather type identification on target days and the forecasting of irradiation intensity and temperature time series. The appropriate regression submodel can be selected based on the subsequent results, and the ultrashort-term real-time forecasting of PV output can be performed over a short time scale. The model incorporates power generation and historical meteorological data from the PV station and is suitable for practical engineering applications. In addition to the irradiation intensity and temperature, other factors related to photovoltaic output are evaluated; however, they are excluded from the model for simplicity and efficiency. The performance of the model is verified by practical modeling analysis.

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Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Cited by 50 publications

References 12 publications

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

Sustainability Evaluation of Power Grid Construction Projects Using Improved TOPSIS and Least Square Support Vector Machine with Modified Fly Optimization Algorithm

A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

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