- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Sky camera geometric calibration using solar observations
摘要： A camera model and associated automated calibration procedure for stationary daytime sky imaging cameras is presented. The specific modeling and calibration needs are motivated by remotely deployed cameras used to forecast solar power production where cameras point skyward and use 180° fisheye lenses. Sun position in the sky and on the image plane provides a simple and automated approach to calibration; special equipment or calibration patterns are not required. Sun position in the sky is modeled using a solar position algorithm (requiring latitude, longitude, altitude and time as inputs). Sun position on the image plane is detected using a simple image processing algorithm. The performance evaluation focuses on the calibration of a camera employing a fisheye lens with an equisolid angle projection, but the camera model is general enough to treat most fixed focal length, central, dioptric camera systems with a photo objective lens. Calibration errors scale with the noise level of the sun position measurement in the image plane, but the calibration is robust across a large range of noise in the sun position. Calibration performance on clear days ranged from 0.94 to 1.24 pixels root mean square error.
关键词： fisheye lens,geometric calibration,camera model,sky camera,solar observations,solar power forecasting
更新于2025-09-23 15:23:52
-
Adaptive Solar Power Forecasting based on Machine Learning Methods
摘要： 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.
关键词： machine learning,k-means,BPNN,adaptive solar power forecasting,LARS
更新于2025-09-23 15:23:52
-
Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks
摘要： As solar photovoltaic (PV) generation becomes cost-effective, solar power comes into its own as the alternative energy with the potential to make up a larger share of growing energy needs. Consequently, operations and maintenance cost now have a large impact on the profit of managing power modules, and the energy market participants need to estimate the solar power in short or long terms of future. In this paper, we propose a solar power forecasting technique by utilizing convolutional neural networks and long–short-term memory networks recently developed for analyzing time series data in the deep learning communities. Considering that weather information may not be always available for the location where PV modules are installed and sensors are often damaged, we empirically confirm that the proposed method predicts the solar power well with roughly estimated weather data obtained from national weather centers as well as it works robustly without sophisticatedly preprocessed input to remove outliers.
关键词： convolutional neural networks,deep learning,long-short term memory,Solar power forecasting
更新于2025-09-23 15:22:29
-
A Hybrid Intelligent Approach for Solar Photovoltaic Power Forecasting: Impact of Aerosol Data
摘要： The penetration of solar photovoltaic (PV) power in distributed generating system is increasing rapidly. The increased level of PV penetration causes various issues like grid stability, reliable power generation and power quality; therefore, it becomes utmost important to forecast the PV power using the meteorological parameters. The proposed model is developed on the basis of meteorological data as input parameters, and the impacts of these parameters have been analyzed with respect to forecasted PV power. The main focus of this research is to explore the performance of optimization-based PV power forecasting models with varying aerosol particles and other meteorological parameters. A newly developed intelligent approach based on grey wolf optimization (GWO) using multilayer perceptron (MLP) has been used to forecast the PV power. The performance of the GWO-based MLP model is evaluated on the basis of statistical indicators such as normalized mean bias error (NMBE), normalized mean absolute error (NMAE), normalized root-mean-square error (NRMSE) and training error. The results of the developed model show the values of NMBE, NMAE and NRMSE as 2.267%, 4.681% and 6.67% respectively. To validate the results, a comparison has been made with particle swarm optimization, Levenberg–Marquardt algorithm and adaptive neuro-fuzzy approach. The performance of the model is found better as compared to other intelligent techniques. The obtained results may be used for demand response applications in smart grid environment.
关键词： Solar power forecasting,Artificial neural network,Distributed power generation,Grey wolf optimization,Solar PV
更新于2025-09-16 10:30:52
-
Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources
摘要： Among renewable energy sources, solar power is rapidly growing as a major power source for future power systems. However, solar power has uncertainty due to the effects of weather factors, and if the penetration rate of solar power in the future increases, it could reduce the reliability of the power system. A study of accurate solar power forecasting should be done to improve the stability of the power system operation. Using the empirical data from solar power plants in South Korea, the short-term forecasting of solar power outputs were carried out for 2016. We performed solar power forecasting with the support vector regression (SVR) model, the na?ve Bayes classifier (NBC), and the hourly regression model. We proposed the ensemble method including the selection of weighting factors for each model to improve forecasting accuracy. The forecasted solar power generation error was indicated using normalized mean absolute error (NMAE) compared to the plant capacity. For the ensemble method, the results of each forecasting model were weighted with the reciprocal of the standard deviation of the forecast error, thus improving the solar power outputs forecast accuracy.
关键词： support vector regression,na?ve Bayes classifier,solar power forecasting,machine learning,ensemble,day ahead power forecasting
更新于2025-09-11 14:15:04
-
LASSO & LSTM Integrated Temporal Model for Short-term Solar Intensity Forecasting
摘要： As a special form of the Internet of Things, Smart Grid is an internet of both power and information, in which energy management is critical for making the best use of the power from renewable energy resources such as solar and wind, while efficient energy management is hinged upon precise forecasting of power generation from renewable energy resources. In this paper, we propose a novel least absolute shrinkage and selection operator (LASSO) and long short term memory (LSTM) integrated forecasting model for precise short-term prediction of solar intensity based on meteorological data. It is a fusion of a basic time series model, data clustering, a statistical model and machine learning. The proposed scheme first clusters data using k-means++. For each cluster, a distinctive forecasting model is then constructed by applying LSTM, which learns the non-linear relationships, and LASSO, which captures the linear relationship within the data. Simulation results with open-source datasets demonstrate the effectiveness and accuracy of the proposed model in short-term forecasting of solar intensity.
关键词： Internet of Things (IoT),Least absolute shrinkage and selection operator (LASSO),Short-term solar power forecasting,Long short term memory (LSTM),K-means++
更新于2025-09-10 09:29:36
-
[IEEE 2018 2nd International Conference On Electrical Engineering (EECON) - Colombo, Sri Lanka (2018.9.28-2018.9.28)] 2018 2nd International Conference On Electrical Engineering (EECon) - Application of Machine Learning Algorithms for Solar Power Forecasting in Sri Lanka
摘要： Reliability and stability of a power system get decrease with the integration of large proportion of renewable energy. Renewable sources such as solar and wind are highly intermittent, and it is difficult to maintain system stability with intolerable proportion of renewable energy injection. Solar power forecasting can be used to improve system stability by providing approximated future power generation to system control engineers and it will facilitate dispatch of hydro power plants in an optimum way. Machine Learning (ML) algorithms have shown great performance in time series forecasting and hence can be used to forecast power using weather parameters as model inputs. This paper presents the application of several ML algorithms for solar power forecasting in Buruthakanda solar park situated in Hambantota, Sri Lanka. The forecasting performance of implemented ML algorithms is compared with Smart Persistence (SP) method and the research shows that the ML models outperforms SP model.
关键词： solar power forecasting,solar power in Sri Lanka,machine learning for forecasting,renewable energy
更新于2025-09-09 09:28:46