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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
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A Simple and Reliable Photovoltaic Forecast for Reliable Power System Operation Control
摘要： Recently various forecasting methods for photovoltaic (PV) generation have been proposed in the literature. However, these standard methods cannot be successfully and widely used in general due to the fact that they require access to specialized data that are not always and everywhere readily available in practice. Furthermore, prediction accuracy of such methods tends to deteriorate specially due to data scarcity. This paper proposes a simple and reliable PV forecasting method using machine learning and neural networks. Confidence interval (CI) results are specifically provided for the local supply-demand control as well as for the robust power system security. The proposed method uses only weather forecasting data that are provided by the Japan Meteorological Agency (JMA) and which is available to the public. The proposed method maintains a high level of accuracy by using real-time correlation data between the specific target and the neighboring areas. Multiple neural networks are constructed based on a weather clustering technique. It has been confirmed through extensive simulation results that the proposed method demonstrates robustness in prediction accuracy and CI effectiveness.
关键词： Confidence intervals,Local energy management,Neural networks,Uncertainties,PV forecasting
更新于2025-09-12 10:27:22
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Estimation of photovoltaic generation forecasting models using limited information
摘要： This work deals with the problem of estimating a photovoltaic generation forecasting model in scenarios where measurements of meteorological variables (i.e., solar irradiance and temperature) at the plant site are not available. A novel algorithm for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant is proposed. Such a method is characterized by a low computational complexity, and efficiently exploits only power generation measurements, a theoretical clear-sky irradiance model, and temperature forecasts provided by a meteorological service. The proposed method is validated on real data.
关键词： Model fitting,Photovoltaic generation,Energy systems,Forecasting
更新于2025-09-12 10:27:22
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A Sensitive and Reliable Carbon Monoxide Monitor for Safety-focused Applications in Coal Mine using a 2.33 μm Laser Diode
摘要： In this paper, a stable and reliable carbon monoxide (CO) monitoring system with high sensitivity (at sub-ppm level) was designed and demonstrated with particular reference to use in the mining industry, tailoring the design specifically for forecasting spontaneous combustion, a major hazard to miners. An appropriate strong CO absorption line was used to minimize the interferences expected from gases present in ambient air, with several preferred CO absorption lines selected and investigated, therefore choosing a distributed feedback (DFB) laser operating at a wavelength of 2330.18 nm as the excitation source. Through a detailed investigation, a minimum detection limit of ~0.2 ppm and a measurement precision of <50 ppb were achieved with a 1 s averaging time. Further long-term continuous monitoring evaluation was carried out, demonstrated the excellent stability and reliability of the developed CO monitor. The results obtained have validated the potential of this design of a CO monitoring system for practical monitoring applications underground to enhance safety in the mining industry.
关键词： mining industry,carbon monoxide,forecasting spontaneous combustion,direct absorption spectroscopy
更新于2025-09-12 10:27:22
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Design of Optimal Power Point Tracking Controller Using Forecasted Photovoltaic Power and Demand
摘要： With the advent of grid-connected Photovoltaic systems for energy generation, new technologies must be created that maintain a continuous and stable balance between supply and demand of generated electricity. Consequently, accurate prediction of solar energy generation and consumption is required. Solar energy generation and electric power demand are both stochastic and non-stationary in nature and often incongruous. The imbalance between demand and supply can be costly and leads to long-term ineffectiveness of power generation and distribution. The aim of this work is to propose methods for maintaining demand-supply balance in PV power generation and distribution systems. To achieve this, we build and combine three different tools: 1) a predictive model for forecasting solar energy generation, 2) a predictive model for demand prediction, and 3) a real-time control algorithm that uses the outputs of prediction models and adjusts the output voltage of PV system to maintain demand- supply balance. Our prediction models are based on time-series forecasting tools and Arti?cial Neural Networks. The control algorithm is called Optimal Power Point Tracking (OPPT) and is based on the Perturb and Observe algorithm. We evaluate the performance of the combined prediction-controller system using real-world data.
关键词： Neural Network,Modeling,Optimal power,Optimization,Fuzzy Logic,Forecasting
更新于2025-09-12 10:27:22
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Time series forecasting of solar power generation for large-scale photovoltaic plants
摘要： Accurate solar power forecasting is essential for grid-connected photovoltaic (PV) systems especially in case of fluctuating environmental conditions. The prediction of PV power output is critical to secure grid operation, scheduling and grid energy management. One of the key elements in PV output prediction is time series analysis especially in locations where the historical solar radiation measurements or other weather parameters have not been recorded. In this work, several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction. Moreover, the effect of prediction time horizon variation for all the algorithms is investigated. Hourly solar power forecasting is carried out to verify the effectiveness of different models. The data utilized in the current work comprises 3640 hours of operation data taken from a 20 MW grid-connected PV station in China.
关键词： neural network,statistical methods,PV power forecasting,time series analysis,deep learning,grid-connected PV plant
更新于2025-09-12 10:27:22
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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
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Short‐term photovoltaic power dynamic weighted combination forecasting based on least squares method
摘要： In recent years, in photovoltaic (PV) power forecasting research, there are certain limitations in single forecasting methods. In traditional combined forecasting methods, such as the average weight combined method and the ?xed weight combined method, the determination of the weight value cannot guarantee that the forecasting error at each moment is the smallest. In order to reduce the PV power forecasting error, this paper proposes a short-term PV power dynamic weighted combination forecasting based on the least squares (LS) method. First, the random components of the PV power are extracted using the periodogram method, and then the dynamic weight value of each method is determined with the LS method. The combined model forecasts the random components of PV power and superimposes them with the periodic component to obtain the ?nal PV power forecast. Using the data from a PV power plant in Ashland, USA, we verify the effectiveness of the proposed method proposed. ? 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
关键词： dynamic weighting,random components of PV power,least squares method,combination forecasting
更新于2025-09-11 14:15:04
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[IEEE 2019 IEEE Milan PowerTech - Milan, Italy (2019.6.23-2019.6.27)] 2019 IEEE Milan PowerTech - Optimal Scheduling of Generators and BESS using Forecasting in Power System with Extremely Large Photovoltaic Generation
摘要： Large scale integration of renewable energy sources (RES) can cause supply demand uncertainty. In Japanese power systems the photovoltaic (PV) generation is growing rapidly. PV forecasting with energy storage systems can be used in Unit Commitment (UC) to reduce these imbalances. In this study Battery Energy Storage systems (BESS) and day-ahead PV forecasting with prediction intervals have been used to examine the imbalances. The day-ahead UC of thermal generators and day-ahead optimal BESS charging and discharging is calculated with different BESS inverter capacities and BESS energy capacities. Then the power shortfall and surplus of PV power in the target day are calculated. The simulation is run for 3 months from April to June 2010 for Kanto area power system of Japan.
关键词： Photovoltaic (PV) forecasting,Unit Commitment (UC),Optimal Power dispatch,Battery Energy Storage Systems (BESS),Prediction Intervals,Mixed Integer Linear Programming (MILP)
更新于2025-09-11 14:15:04
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Performance of Turbulence Models in Simulating Wind Loads on Photovoltaics Modules
摘要： The performance of ?ve conventional turbulence models, commonly used in the wind industry, are examined in predicting the complex wake of an in?nite span thin normal ?at plate with large pressure gradients at Reynolds number of 1200. This body represents a large array of Photovoltaics modules, where two edges of the plate dominate the ?ow. This study provided a benchmark for capabilities of conventional turbulence models that are commonly used for wind forecasting in the wind energy industry. The results obtained from Reynolds Averaged Navier-Stokes (RANS) k ? ε, Reynolds Normalization Group (RNG) k ? ε, RANS k ? ω Shear Stress Transport (SST) and Reynolds Stress Model (RSM) were compared with existing Direct Numerical Simulations (DNS). The mean ?ow features and unsteady wake characteristics were used as testing criteria amongst these models. All turbulence models over-predicted the mean recirculation length and under-predicted the mean drag coef?cient. The major differences between numerical results in predicting the mean recirculation length, mean drag and velocity gradients, leading to de?cits in turbulence kinetic energy production and diffusion, hint at major dif?culties in modeling velocity gradients and thus turbulence energy transport terms, by traditional turbulence models. Unsteadiness of ?ow physics and nature of eddy viscosity approximations are potential reasons. This hints at the de?ciencies of these models to predict complex ?ows with large pressure gradients, which are commonly observed in wind and solar farms. The under-prediction of wind loads on PV modules and over-estimation of the recirculation length behind them signi?cantly affects the ef?ciency and operational feasibility of solar energy systems.
关键词： RANS,CFD,PV module,wake dynamics,turbulence,wind loads,wind forecasting
更新于2025-09-11 14:15:04