- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Impact of DNI forecasting on CSP tower plant power production
摘要： In the context of energy policies focusing on minimizing power plant emissions, concentrating solar power (CSP) technology plays an important role in the energy mix. These plants require a high level of direct normal irradiance to work properly and profitably. Over-sizing of plant capacity is frequently employed in order to store part of the energy produced, to extend the operating time throughout the day, and also to manage cloud transients. Forecasting the energy delivered by the plant is very important in plant operational strategies to ensure dispatchability as much as possible. This work presents an analysis of energy forecasting in solar tower plants by combining a short-term solar irradiation forecasting scheme with a solar tower plant model using the System Advisor Model (SAM), as the modelling tool for computing plant production throughout the year. Satellite images were used to predict Direct Normal Irradiance (DNI) on an intra-hour time-scale (up to three hours). The predictions were introduced into SAM to simulate the behaviour of the Gemasolar and Crescent Dunes plants, placed on Spain and Nevada, respectively). The results show that the best outcomes appear for the 90-mins horizon, where the Mean Bias was about -10% and the RMSE near to 23%.
关键词： Crescent Dunes (Nevada),CSP tower plant,DNI Forecasting,System Advisor Model,Gemasolar (Spain),Power Output prediction
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) - Miramar Beach, FL, USA (2019.8.19-2019.8.21)] 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) - Invited Talk: "High Resolution Space/Time Imaging of Shockwaves Generated by Remote Laser Plasmas Produced by Light Filaments"
摘要： In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
关键词： PCA,dimensionality reduction,electric load prediction,smart grid,genetic algorithm,forecasting,echo state network,Time-series
更新于2025-09-19 17:13:59
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[IEEE 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Harbin, China (2019.8.11-2019.8.14)] 2019 22nd International Conference on Electrical Machines and Systems (ICEMS) - Power Forecasting of Photovoltaic Generation Based on Multiple Linear Regression Method with Real-time Correction Term
摘要： This paper proposes a photovoltaic power generation forecasting model which improves Multiple Linear Regression method (MLRM) with real-time correction term traditional day-ahead, hourly power (RCT). Firstly, a generation prediction model is developed by MLRM based on qualitative variables (hour, month, weather type), quantitative variable (solar radiation intensity) and physical characteristics of interactions between the variables. Secondly, an improved is presented which adds a model named MLRM+RCT correction term based on shorter real-time measured power data to MLRM to reduce the hourly prediction errors of MLRM. MLRM+RCT is tested based on power generation data released by IEEE Energy Forecasting Group in 2014. The results show that the performance of MLRM+RCT is better than that of MLRM and a benchmark method called exponential smoothing method.
关键词： Photovoltaic system,real-time correction term,Multiple Linear Regression method,short-term forecasting
更新于2025-09-19 17:13:59
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Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model
摘要： The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.
关键词： solar irradiation,adaptive neuro-fuzzy inference systems,PVs power output forecasting,particle swarm optimization-artificial neural networks
更新于2025-09-19 17:13:59
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Accurate Output Forecasting Method for Various Photovoltaic Modules Considering Incident Angle and Spectral Change Owing to Atmospheric Parameters and Cloud Conditions
摘要： Because semiconductors absorb wavelengths dependent on the light absorption coefficient, photovoltaic (PV) energy output is affected by the solar spectrum. Therefore, it is necessary to consider the solar spectrum for highly accurate PV output estimation. Bird’s model has been used as a general spectral model. However, atmospheric parameters such as aerosol optical depth and precipitable water have a constant value in the model that only applies to clear days. In this study, atmospheric parameters were extracted using the Bird’s spectrum model from the measured global spectrum and the seasonal fluctuation of atmospheric parameters was examined. We propose an overcast spectrum model and calculate the all-weather solar spectrum from clear to overcast sky through linear combination. Three types of PV modules (fixed Si, two-axis tracking Si, and fixed InGaP/GaAs/InGaAs triple-junction solar cells) were installed at the University of Miyazaki. The estimated performance ratio (PR), which takes into account incident angle and spectral variations, was consistent with the measured PR. Finally, the energy yield of various PVs installed across Japan was successfully estimated.
关键词： output forecasting,incident angle,energy yield,photovoltaic,precipitable water,aerosol optical depth
更新于2025-09-19 17:13:59
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A Gaussian-Gaussian-Restricted-Boltzmann-Machine-based Deep Neural Network Technique for Photovoltaic System Generation Forecasting
摘要： This paper proposes a new Gaussian-Gaussian-Restricted-Boltzmann-Machine-based method for forecasting photovoltaic (PV) system generation forecasting. Although renewable energy such as PV system and wind power generation has been used to suppress greenhouse gases in the world, it has a drawback that weather conditions influence the generation output significantly. Thus, it is not easy to perform Economic Load Dispatch (ELD) and Unit Commitment in power systems smoothly. From a standpoint of power system operation, more accurate predication models are required to deal with predicted values of PV system generation. In this paper, an efficient Deep Neural Network (DNN) model with Gaussian Gaussian Restricted Boltzmann Machine is presented to predict one-step-ahead PV system generation output. The model is based on Restricted Boltzmann Machine as a feature extractor and Multi-Layer Perceptron (MLP) as ANN. The effectiveness of the proposed method is demonstrated for real data of a PV system.
关键词： Solar energy,Forecasting,Time-series analysis,Artificial Intelligence,Power systems
更新于2025-09-19 17:13:59
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Forecasting the Performance of a Photovoltaic Solar System Installed in other Locations using Artificial Neural Networks
摘要： Photovoltaic solar energy has been spread all over the world, and in Brazil this energy source has been getting considerable space in the last years, being driven mainly by the energy crises. However, when installed in regions with low incidence of solar irradiation, this technology presents a loss of efficiency in the generation of energy. As an alternative to this consideration, a power prediction study could be conducted prior to its installation, based on local climate information that directly influences power generation, verifying the feasibility of system implementation and avoiding unrewarded investment. Therefore, the objective of this work is to predict the viability of the installation of a photovoltaic system of 3kWp in different places, with the assist of an Artificial Neural Network. Thus, the feedforward network was used for the training, being trained and validated with the support of MatlabVR , and inserting samples of temperature and solar irradiation as input variables. Through the performance methods, the results are favorable for this application, presenting validations with RMSE% in the range of 13-20% and R of not less than 0.93. The predictions presented RMSE% around 19-25% and average powers close to the real values generated by the PV system.
关键词： solar irradiation,renewable energy,electrical systems,energy efficiency,power forecasting,feedforward,artificial neural network,root mean square error,solar photovoltaic system,distributed generation
更新于2025-09-19 17:13:59
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[IEEE 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Shenzhen, China (2019.11.25-2019.11.27)] 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Research on a Smart LED Lighting Based on Improved Flyback Driver
摘要： In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
关键词： genetic algorithm,forecasting,PCA,echo state network,Time-series,smart grid,electric load prediction,dimensionality reduction
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE) - Osaka, Japan (2019.10.15-2019.10.18)] 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE) - Design of a Real-Time Visible Laser Light Communication System with Basedband in FPGA for High Definition video Transmission
摘要： The solar power penetration in distribution grids is growing fast during the last years, particularly at the low-voltage (LV) level, which introduces new challenges when operating distribution grids. Across the world, distribution system operators (DSO) are developing the smart grid concept, and one key tool for this new paradigm is solar power forecasting. This paper presents a new spatial–temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers. The scope is 6-h-ahead forecasts at the residential solar photovoltaic and medium-voltage (MV)/LV substation levels. This framework has been tested in the smart grid pilot of évora, Portugal, and using data from 44 microgeneration units and 10 MV/LV substations. A benchmark comparison was made with the autoregressive forecasting model (AR—univariate model) leading to an improvement on average between 8% and 10%.
关键词： spatial–temporal,forecasting,solar power,smart metering,smart grid,Distribution network
更新于2025-09-19 17:13:59
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Solar photovoltaic power output forecasting using machine learning technique
摘要： Photovoltaic (PV) systems are used around the world to generate solar power. Solar power sources are irregular in nature due to the output power of PV systems being intermittent and depending greatly on environmental factors. These factors include, but are not limited to, irradiance, humidity, PV surface temperature, speed of the wind. Due to uncertainties in the photovoltaic generation, it is critical to precisely envisage the solar power generation. Solar power forecasting is necessary for supply and demand planning in an electric grid. This prediction is highly complex and challenging as solar power generation is weather-dependent and uncontrollable. This paper describes the effects of various environmental parameters on the PV system output. Prediction models based on Artificial Neural Networks (ANN) and regression models are evaluated for selective factors. The selection is done by using the correlation-based feature selection (CSF) and ReliefF techniques. The ANN model outperforms all other techniques that were discussed.
关键词： solar photovoltaic,regression models,Artificial Neural Networks,power output forecasting,machine learning
更新于2025-09-19 17:13:59