- 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
-
Assessment of different combinations of meteorological parameters for predicting daily global solar radiation using artificial neural networks
摘要： In this study, for determining the best-input scenarios of the used parameters in predicting the Daily Global Solar Radiation (DGSR), a new approach based on Artificial Neural Networks (ANNs) was presented. The proposed approach is based on comparisons between all possible input combinations for determining the best scenarios that can give perfect correlations and approximations with DGSR. Recorded data from 35 stations belonging to different climatic zones (27 in Morocco and 8 in neighboring countries) were reported for training and testing the obtained results. The used input parameters include geographical coordinates, sun declination, day length, day number, clearness index (KI), Top Of Atmosphere (TOA), average ambient temperature (Ta), maximum temperature (Tmax), minimum temperature (Tmin), difference temperature (ΔT), temperature ratio (TR), relative humidity (Rh) and wind speed (Ws). The results revealed 128 best-input scenarios, where the first relevant input combination was found for KI, Ta, ΔT, TR and TOA. This result indicated that the best-input scenario for predicting DGSR is based only on three climatological parameters: KI, function of Ta f(Ta) and TOA. In addition, based on these found best-input scenarios and on the least square regression (LSR) technique, 128 new linear relationships between DGSR and the found best-input combinations were developed. The statistical analysis expressed through statistical criteria indicated perfect correlations and approximations between the predicted and measured values of DGSR.
关键词： Best scenarios,ANNs,Least square regression,Statistical analysis,Solar radiation modelling,Forecasting
更新于2025-09-23 15:23:52
-
Reconciling solar forecasts: Sequential reconciliation
摘要： When forecasting hierarchical photovoltaic (PV) power generation in a region and/or over several forecast horizons, reconciliation is needed to ensure the lower-level forecasts add up exactly to the upper-level forecasts. Previously in “Reconciling solar forecasts: Geographical hierarchy” [Sol. Energy 146 (2017) 276–286] and “Reconciling solar forecasts: Temporal hierarchy” [Sol. Energy 158 (2017) 332–346], forecast reconciliation has been demonstrated for geographical and temporal hierarchies, separately. This article follows such frameworks and extends the reconciliation to spatio-temporal cases. More specifically, sequential reconciliation is used for operational day-ahead forecasting of 318 PV systems in California. It is shown that by using sequential reconciliation, aggregate-consistent forecasts can be obtained across both the geographical and temporal hierarchies. In addition, the forecast accuracy can be further improved from that of the single-hierarchy cases.
关键词： Forecast reconciliation,Numerical weather prediction,Operational forecasting
更新于2025-09-23 15:23:52
-
[IEEE 2018 IEEE Congress on Evolutionary Computation (CEC) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 IEEE Congress on Evolutionary Computation (CEC) - Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models
摘要： Development and improvement of solar forecasting models have been extensively addressed in the past years due to the importance of solar energy as a renewable energy source. This work presents an application and improvement of intra-day solar predictive models based on genetic programming. Forecasts were evaluated in time horizons of 10 minutes up to 180 minutes ahead as future steps at two completely different locations: one in northern hemisphere and another in the southern hemisphere. The improvement strategy was validated in comparison of error metrics to the ones obtained by benchmark methods of solar forecasting. The proposed model results will be presented and validated for each considered location.
关键词： solar forecasting,short-term forecasting,multigene genetic programming,intra-day forecasting
更新于2025-09-23 15:22:29
-
[IEEE 2018 IEEE Power & Energy Society General Meeting (PESGM) - Portland, OR, USA (2018.8.5-2018.8.10)] 2018 IEEE Power & Energy Society General Meeting (PESGM) - A Comparison Study of Dispatching Various Battery Technologies in a Hybrid PV and Wind Power Plant
摘要： This paper presents a comparison study for various types of battery technologies using selected BESS parameters in a hybrid PV and wind power plant taking the aging of the batteries into account. The optimization problem was reformulated as a Mixed Integer Linear Programming (MILP) problem, with the aim being to minimize the overall operating cost. IBM CPLEX was used to solve the problem. For forecasting, two techniques, i.e., the persistence and Elman Neural Network, were applied for both day-ahead and hour-ahead forecasting. Moreover, four case studies were performed, including the scenario without battery, day-ahead dispatch, day-ahead dispatch with a rolling horizon and hour-ahead dispatch. The simulation results showed that hour-ahead dispatch demonstrated the highest profitability. Furthermore, among different battery technologies, lithium-ion batteries resulted in the highest operating profits, whereas lead-acid batteries had the shortest payback period and vanadium redox flow batteries demonstrated the least degradation from cycling.
关键词： Battery Energy Storage System,Hybrid PV and Wind Power Plant,Battery Scheduling,Forecasting Errors
更新于2025-09-23 15:22:29
-
Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
摘要： In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (ANN) model was built to calculate this data from more available ones. For the estimation of 5-min GHI, the normalized root mean square error (nRMSE) of the 6-inputs model is 19.35%. As solar collectors are often tilted, a second ANN model was developed to transform GHI into global tilted irradiation (GTI), a difficult task due to the anisotropy of scattering phenomena in the atmosphere. The GTI calculation from GHI was realized with an nRMSE around 8% for the optimal configuration. These two models estimate solar data at time, t, from other data measured at the same time, t. For an optimal management of energy, the development of forecasting tools is crucial because it allows anticipation of the production/consumption balance; thus, ANN models were developed to forecast hourly direct normal (DNI) and GHI irradiations for a time horizon from one hour (h+1) to six hours (h+6). The forecasting of hourly solar irradiation from h+1 to h+6 using ANN was realized with an nRMSE from 22.57% for h+1 to 34.85% for h+6 for GHI and from 38.23% for h+1 to 61.88% for h+6 for DNI.
关键词： solar irradiation,estimation,meteorological data,short time step,forecasting
更新于2025-09-23 15:22:29
-
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 Short Term Day-Ahead Solar Radiation Prediction Using Machine Learning Techniques
摘要： The task of solar power forecasting becomes vital to ensure grid constancy and to enable an optimal unit commitment and cost-effective dispatch. Each year latest techniques and approaches appear to increase the exactitude of models with the important goal of reducing uncertainty in the predictions. The aim of the paper is to compile a big part of the knowledge about solar power forcing, to focus on the most recent advancements and future trends. Firstly, the inspiration to achieve an accurate forecast is presented with the analysis of the economic implications it may have. To address the problem superlative prediction models are rummaged by us using machine learning techniques. We make a comparison between multiple regression techniques for creating prediction models, along with linear least squares and support vector machines using multiple kernel functions. Predictions are analyzed by us in our experiments for the day ahead solar radiation data and it is shown that a machine learning approach yields feasible results for short-term solar prediction. The proposed model achieves a root mean square error improvement of around 29% compared to others proposed model except one.
关键词： Forecasting,SVR,Renewable energy,Short-term,Machine learning
更新于2025-09-23 15:22:29
-
Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting
摘要： A deep recurrent neural network with long short-term memory units (DRNN-LSTM) model is developed to forecast aggregated power load and the photovoltaic (PV) power output in community microgrid. Meanwhile, an optimal load dispatch model for grid-connected community microgrid which includes residential power load, PV arrays, electric vehicles (EVs), and energy storage system (ESS), is established under three different scheduling scenarios. To promote the supply-demand balance, the uncertainties of both residential power load and PV power output are considered in the model by integrating the forecasting results. Two real-world data sets are used to test the proposed forecasting model, and the results show that the DRNN-LSTM model performs better than multi-layer perception (MLP) network and support vector machine (SVM). Finally, particle swarm optimization (PSO) algorithm is used to optimize the load dispatch of grid-connected community microgrid. The results show that EES and the coordinated charging mode of EVs can promote peak load shifting and reduce 8.97% of the daily costs. This study contributes to the optimal load dispatch of community microgrid with load and renewable energy forecasting. The optimal load dispatch of community microgrid with deep learning based solar power and load forecasting achieves total costs reduction and system reliability improvement.
关键词： community microgrid,load forecasting,deep learning,Optimal load dispatch,solar power
更新于2025-09-23 15:22:29