scholarly journals A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4815 ◽  
Author(s):  
Yosui Miyazaki ◽  
Yusuke Kameda ◽  
Junji Kondoh
Keyword(s):  
Power Systems ◽  
Output Power ◽  
Power Output ◽  
Percentage Error ◽  
Support Vector ◽  
Short Term ◽  
Pv System ◽  
Forecasting Method ◽  
Geographically Distributed ◽  
Power Forecasting

The number of photovoltaic (PV) power systems being installed worldwide has been increasing. This has resulted in maintenance of an adequate balance between demand and supply becoming a great concern for power system operators. Forecasting PV power outputs is a promising countermeasure that has been garnering significant interest. Conventional methods for achieving this often use learning methods, such as neural networks and support vector regression. In contrast, this paper proposes a short-term power-forecasting method for geographically distributed PV systems that uses only their previous output power data. In the proposed method, first, the ratio of the power generation output to the maximum power output value for each observation instance in a designated period for each PV system at a certain date and time is obtained. Then, the future geographical distribution of the ratio is predicted from the temporal change (motion) of the preceding distribution. Finally, the predicted ratio is reconverted into the power output to perform short-term power forecasting. The results of total PV output power prediction in the Kanto area of Japan indicate that the proposed method has an average mean absolute percentage error of 4.23% and root mean square error of 0.69 kW, which verifies its efficacy.

Short-Term Power Forecasting of Small-Scale Hydropower Based on Projection Pursuit Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 764-769
Author(s):  
Wen Xia Liu ◽  
Xi Zhou ◽  
Xiao Bo Xu ◽  
Mei Mei Xu
Keyword(s):  
Output Power ◽  
Load Forecasting ◽  
Projection Pursuit ◽  
Forecast Accuracy ◽  
Small Scale ◽  
Forecasting Model ◽  
Practical Application ◽  
Short Term ◽  
Forecasting Method ◽  
Power Forecasting

Compared with other traditional energy, the small-scale hydropower which is intermittent energy can not be stored and scheduled. The greater fluctuant of the output power of small-scale hydropower leads to great difficult to the operation of the power system. Most of the existing small-scale hydropower forecasting is considered as the load forecasting factors, and there is not effective forecasting method. This paper establishes an output power forecasting model of the small-scale hydropower based on Projection Pursuit. The simulation results show that the new algorithm has a strong practical application in the small-scale hydropower output power forecasting and the forecast accuracy meets the scheduling requirements.

PV System Power Forecasting Based on Neural Network with Fuzzy Processing of Weather Factors

2013 ◽  
Vol 860-863 ◽  
pp. 172-175
Author(s):  
Yong Qiang Hu ◽  
Ming Yu Wang
Keyword(s):  
Neural Network ◽  
Weather Data ◽  
Membership Functions ◽  
Short Term ◽  
Weather Factors ◽  
Pv System ◽  
Forecasting Method ◽  
Temperature Radiation ◽  
System Power ◽  
Power Forecasting

A short-term PV system power forecasting method is presented in the paper based on neural network considering fuzzy characteristics of weather factors. Weather factors that affect PV system power output mainly include temperature, radiation intensity, rain and relative humidity which are all of strong fuzziness. The paper firstly made use of membership functions to process their fuzziness. Then, the historical power data of a PV system was put into neural network together with fuzzy processed historical weather data to train the network, therefore, neural network that be able to forecast PV power was get. Finally, data of an actual PV system in Colorado was employed to methods with and without fuzzy processing of weather factors, results show that the method with fuzzy processing is more accurate than that without fuzzy processing.

scholarly journals Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4733
Author(s):  
Andi A. H. Lateko ◽  
Hong-Tzer Yang ◽  
Chao-Ming Huang ◽  
Happy Aprillia ◽  
Che-Yuan Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Weather Data ◽  
Support Vector ◽  
Short Term ◽  
Learner Model ◽  
Average Maximum ◽  
Power Forecasting

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

scholarly journals Model for Predicting the Operating Temperature of Stratospheric Airship Solar Cells with a Support Vector Machine

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1228
Author(s):  
Xuwei Wang ◽  
Zhaojie Li ◽  
Yanlei Zhang
Keyword(s):  
Support Vector Machine ◽  
Solar Cell ◽  
Energy Balance ◽  
Output Power ◽  
Operating Temperature ◽  
Support Vector ◽  
Power Calculation ◽  
Pv System ◽  
Stratospheric Airship ◽  
Cell Modules

The stratospheric airship is a kind of aircraft that completely relies on the cycle of photovoltaic energy systems to achieve long duration flight. The accurate estimation of the operating temperature of solar cell modules on stratospheric airship is extremely important for the design of photovoltaics system (PV system), the output power calculation of PV system, and the calculation of energy balance. However, the related study has been rarely reported. A support vector machine prediction method based on particle swarm optimization algorithm (PSO-SVM) was established to predict the operating temperature of solar cell modules on stratospheric airship. The PSO algorithm was used to dynamically optimize the SVM’s parameters between the operating temperature of the solar cell modules and the measured data such as atmospheric pressure, solar radiation intensity, flight speed, and ambient temperature. The operating temperature data of the two sets of solar cell modules measured in the flight test were used to verify the accuracy of the temperature prediction model, and the prediction results were compared with a back propagation neural network (BPNN) method and the simulation results calculated by COMSOL Multiphysics of COMSOL, Inc., Columbus, MA, USA. The results shown that the PSO-SVM model realized the accurate prediction of the operating temperature of solar cell modules on stratospheric airship, which can guide the design of PV system, the output power calculation of PV system, and the calculation of energy balance.

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

scholarly journals Adaptive Solar Power Forecasting based on Machine Learning Methods

2018 ◽  
Vol 8 (11) ◽  
pp. 2224 ◽  
Author(s):  
Yu Wang ◽  
Hualei Zou ◽  
Xin Chen ◽  
Fanghua Zhang ◽  
Jie Chen
Keyword(s):  
Power Systems ◽  
Solar Power ◽  
Machine Learning Methods ◽  
Load Demand ◽  
Forecasting Method ◽  
Combining Data ◽  
Data Updating ◽  
Solar Power Forecasting ◽  
Forecasting Errors ◽  
Power Forecasting

Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

scholarly journals Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Ning ◽  
Guanghao Lu ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Hualei Wang
Keyword(s):  
Power Systems ◽  
Spatial Correlation ◽  
Data Storage ◽  
Power Plants ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Storage Condition ◽  
Support Vector ◽  
Forecast Method ◽  
Short Term

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

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