scholarly journals Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhifeng Zhong ◽  
Chenxi Yang ◽  
Wenyang Cao ◽  
Chenyang Yan
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Prediction Accuracy ◽  
Particle Swarm Optimization Algorithm ◽  
Theory Model ◽  
Grey Model ◽  
Grey Theory ◽  
Short Term ◽  
Swarm Optimization ◽  
Photovoltaic Power

Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.

Design of Photovoltaic Power Generation Forecasting Model Based on Multivariable Grey Theory

2014 ◽  
Vol 953-954 ◽  
pp. 3-7 ◽  
Author(s):  
Zhi Feng Zhong ◽  
Chen Yang Yan ◽  
Tian Jin Zhang ◽  
Mao Tian
Keyword(s):  
Power Generation ◽  
Engineering Application ◽  
Theory Model ◽  
Forecasting Model ◽  
Good Prediction ◽  
Grey Theory ◽  
Power Station ◽  
Short Term ◽  
Photovoltaic Power ◽  
Short Term Prediction

This paper makes a prediction about the daily power generation of the photovoltaic power station in Wuhan International Exhibition Center by the application of multivariable grey theory model and compares the forecasted results with the real results. This research proves that the application of the theory of multivariable grey model on short-term prediction of photovoltaic power generation can achieve a good prediction effect, which also has certain engineering application value.

Grey Model Based Particle Swarm Optimization Algorithm for Anticorrosion Reliability Design of Underground Pipelines

2010 ◽  
Vol 118-120 ◽  
pp. 541-545
Author(s):  
Qin Ming Liu ◽  
Ming Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Grey Model ◽  
Swarm Optimization ◽  
Reliability Design ◽  
Model Based ◽  
Underground Pipelines

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines’ corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

scholarly journals Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 6 ◽  
Author(s):  
Min Huang ◽  
Zhen Liu
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Prediction Accuracy ◽  
Particle Swarm Optimization Algorithm ◽  
Evidence Theory ◽  
Fault Prediction ◽  
Swarm Optimization ◽  
Sensing Data ◽  
Vibration Sensing

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model’s ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

scholarly journals A Predictive Analysis of the Business Environment of Economies along the Belt and Road Using the Fractional-Order Grey Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lingling Pei ◽  
Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Particle Swarm Optimization Algorithm ◽  
Business Environment ◽  
Grey Model ◽  
Predictive Analysis ◽  
Optimal Order ◽  
Swarm Optimization ◽  
Belt And Road ◽  
The Belt And Road

This paper determined the optimal order of FGM (1, 1) model through particle swarm optimization algorithm and combined with the World Bank business environment data to predict and analyze the business environment of economies along the Belt and Road. The empirical results show that the FGM (1, 1) model has a good predicting effect on the business environment. In terms of prediction accuracy, the FGM (1, 1) model based on particle swarm optimization algorithm to determine the optimal order is significantly better than the traditional GM (1, 1) model. The predict results show that the business environment level of economies along the Belt and Road will increase year by year from 2021 to 2022, but the overall level is still relatively low. The main innovation of this paper lies in the introduction of the fractional-order grey model into the predictive analysis of the business environment, which is of great significance to the extension and application of fractional-order models in management and economic systems.

scholarly journals An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4480
Author(s):  
Qun Niu ◽  
Han Wang ◽  
Ziyuan Sun ◽  
Zhile Yang
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Thermal Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Solar Thermal Power ◽  
Bare Bone

Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popular forms to utilize solar energy, yet its modelling is a complicated problem. In this paper, an improved bare bone multi-objective particle swarm optimization algorithm (IBBMOPSO) is proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO). The algorithm is first tested on a set of benchmark problems, confirming its efficacy and the convergency speed. Then, it is applied to optimize two typical solar power generation systems including the solar Stirling power generation and the solar Brayton power generation; the results show that the proposed algorithm outperforms other algorithms for multi-objective optimization problems.

Sign in / Sign up

Export Citation Format

Share Document