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- 摘要
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- 实验方案
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Enhanced Photovoltaic Power Model Fidelity Using On-Site Irradiance and Degradation-Informed Performance Input
摘要： The impact of irradiance data collection location and degradation-informed module performance on irradiance-to-power model accuracy is evaluated. A decrease in RMSE of over 20% is found for output power predictions using NREL PVWatts when site-specific irradiance data are coupled with maximum power point output characteristics obtained using accelerated lifecycle testing.
关键词： photovoltaic cells,solar power generation,forecasting,degradation
更新于2025-09-19 17:13:59
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Machine learning algorithms for predicting the amplitude of chaotic laser pulses
摘要： Forecasting the dynamics of chaotic systems from the analysis of their output signals is a challenging problem with applications in most fields of modern science. In this work, we use a laser model to compare the performance of several machine learning algorithms for forecasting the amplitude of upcoming emitted chaotic pulses. We simulate the dynamics of an optically injected semiconductor laser that presents a rich variety of dynamical regimes when changing the parameters. We focus on a particular dynamical regime that can show ultrahigh intensity pulses, reminiscent of rogue waves. We compare the goodness of the forecast for several popular methods in machine learning, namely, deep learning, support vector machine, nearest neighbors, and reservoir computing. Finally, we analyze how their performance for predicting the height of the next optical pulse depends on the amount of noise and the length of the time series used for training.
关键词： chaotic systems,laser pulses,reservoir computing,deep learning,forecasting,support vector machine,machine learning,nearest neighbors
更新于2025-09-16 10:30:52
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[IEEE 2019 International Conference on Information and Communications Technology (ICOIACT) - Yogyakarta, Indonesia (2019.7.24-2019.7.25)] 2019 International Conference on Information and Communications Technology (ICOIACT) - Performance Analysis of Grid Interfaced Photovoltaic Systems for Reliable Agri-Microgrids using PVsyst
摘要： Today most of the countries are fulfilling their energy need from fossil fuel resources, which are vastly depleting and era of fossil fuel is gradually ending. Power generation using fossil fuel is creating harmful effect on environment, leading to global warming which has become matter of great concern. Massive use of these fuels, release large amount of waste heat, causing thermal pollution in surrounding area, leading to destroy of various types of plant and animal life. In case of nuclear power plants, disposal of radioactive wastes and release of radioactivity during accident creates long term sever problems. Therefore every country is now embarking on search of alternative sources of energy, specifically, solar energy, which is inexhaustible source of energy and freely available in almost all parts of the world. Our research team suggested solar PV plant of 10 Kwp for Shree Samarth Agro-Tech Foundation, Which is a small scale Agricultural based business enterprise operated by farmer’s self-help group, located in Solapur District of Maharashtra State, India. Thus, our research team, tried to provide technology up gradation to these conventional diesel generator powered jaggery making units that helps in reducing green house gas (GHG) emission. Detailed performance analysis of the proposed system will be carried out using PVsyst simulation tool.
关键词： Solar PV System,PVsyst,yield forecasting,Jaggery units,Performance ratio
更新于2025-09-16 10:30:52
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Economic Sustainability Study of S?o Miguel Island in the Azores Using Photovoltaic Panels and Wind Turbines
摘要： Currently, the nine islands of the Autonomous Region of the Azores have fossil fuel power stations as their main source of electric power. Each island has an independent electrical system classified as an isolated micro-system, given its size and location. The goal of this study is to analyze the best set of technologies for a sustainable hybrid system. This study will be applied first on S?o Miguel to make the largest island of the archipelago 100% renewable. We will consider factors such as the island’s actual data production, economic scenarios, growth perspectives of consumption and reliability of supply.
关键词： renewable energies,energy consumption,isolated micro-network,forecasting method,network stability
更新于2025-09-16 10:30:52
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Combined probabilistic forecasting method for photovoltaic power using an improved Markov chain
摘要： A novel combined probabilistic forecasting method based on an improved Markov chain for photovoltaics (PVs) to enhance the accuracy of probabilistic PV power forecasting is presented. First, a Markov chain (MC) forecasting structure combining precise factors is proposed that considers more influence factors beyond the statistical information of historical data as compared with conventional MCs. Rough set theory is then used to refine the major factors to quantify the influence of those factors. Furthermore, a k-nearest neighbours algorithm is used to select similar samples for building an accurate forecasting model. Based on similar samples, the changing PV trends are more obvious than when using whole historical samples, thus further improving forecasting accuracy. Finally, the effectiveness and superiority of the proposed method are verified by comparing results from simulations with the results from competing methods for two cases using datasets from DESERT KNOWLEDGE AUSTRALIA Solar Centre and GEFCom2014. The simulation results show that the proposed method can provide probabilistic forecasting results with better performance, also, the proposed method can be adapted to various forecasting scenarios.
关键词： photovoltaic power,k-nearest neighbours algorithm,probabilistic forecasting,rough set theory,improved Markov chain
更新于2025-09-16 10:30:52
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Photovoltaic power forecasting based LSTM-Convolutional Network
摘要： The volatile and intermittent nature of solar energy itself presents a significant challenge in integrating it into existing energy systems. Accurate photovoltaic power prediction plays an important role in solving this problem. With the development of deep learning, more and more scholars have applied the deep learning model to time series prediction and achieved very good results. In this paper, a hybrid deep learning model (LSTM-Convolutional Network) is proposed and applied to photovoltaic power prediction. In the proposed hybrid prediction model, the temporal features of the data are extracted first by the long-short term memory network, and then the spatial features of the data are extracted by the convolutional neural network model. In order to show the superior performance of the proposed hybrid prediction model, the prediction results of the hybrid model are compared with those of the single model (long-short term memory network, convolutional neural network) and the hybrid network (Convolutional-LSTM Network) model, and the results of eight error evaluation indexes are given. The results show that the hybrid prediction model has better prediction effect than the single prediction model, and the proposed hybrid model (extract the temporal characteristics of data first, and then extract the spatial characteristics of data) is better than Convolutional-LSTM Network (extract the spatial characteristics of data first, and then extract the temporal characteristics of data).
关键词： Convolutional-LSTM network,LSTM-Convolutional network,Photovoltaic power forecasting,Convolutional neural network,Deep learning,Long-short term memory
更新于2025-09-16 10:30:52
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[IEEE 2019 AEIT International Annual Conference (AEIT) - Florence, Italy (2019.9.18-2019.9.20)] 2019 AEIT International Annual Conference (AEIT) - Multi-Layer RNN-based Short-term Photovoltaic Power Forecasting using IoT Dataset
摘要： Photovoltaic power ?uctuation in daytime is one of critical problems for the ef?cient and stable operation of the smart grid. To respond the PV power ?uctuation resulting from weather change, the short-term PV power forecasting algorithm using multi-layer RNN is proposed in this paper. It consists of multiple RNN layers using power and meteorological data which are collected by on-site IoT (Internet of Things) sensors. Experimental results showed that the accuracies of the short-term PV power prediction of 5 minutes and 1 hour later using 3 RNN layers with 12 time-step were 98.02% and 96.58% based on the normalized RMSE, respectively. These experimental results con?rmed that the proposed short-term prediction algorithm using multi-layer RNN model was applicable to respond the short-term PV power ?uctuation.
关键词： IoT (Internet of Things),multi-layer RNN,PV forecasting algorithm,photovoltaic power
更新于2025-09-16 10:30:52
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A Novel Approach for Seamless Probabilistic Photovoltaic Power Forecasting Covering Multiple Time Frames
摘要： Uncertainty in the upcoming production of photovoltaic (PV) plants is a challenge for grid operations and also a source of revenue loss for PV plant operators participating in electricity markets, since they have to pay penalties for the mismatch between contracted and actual productions. Improving PV predictability is an area of intense research. In real-world applications, forecasts are often needed for different time frames (horizon, update frequency, etc.) and are derived by dedicated models for each time frame (i.e. for day ahead and for intra-day trading). This can result in both different forecasted values corresponding to the same horizon and discontinuities among time-frames. In this paper we address this problem by proposing a novel seamless probabilistic forecasting approach able to cover multiple time frames. It is based on the Analog Ensemble (AnEn) model, however it is adapted to consider the most appropriate input for each horizon from a pool of available input data. It is designed to be able to start at any time of day, for any forecast horizon, making it well-suited for applications like continuous trading. It is easy to maintain as it adapts to the latest data and does not need regular retraining. We enhance short-term predictability by considering data from satellite images and in situ measurements. The proposed model has low complexity compared to benchmark models and is trivially parallelizable. It achieves performance comparable to state-of-the-art models developed speci?cally for the short term (i.e. up to 6 hours) and the day ahead. The evaluation was carried out on a real-world case comprising three PV plants in France, over a period of one year.
关键词： Probabilistic Forecasting,Satellite Imagery,Photovoltaics,Analog-Ensemble Model
更新于2025-09-16 10:30:52
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - A Novel Ship-rocking Forecasting Method based on Hilbert Transform
摘要： The ship-rocking is a crucial factor which affects the accuracy of the ocean-based aerospace vehicle measurement. Here we have analysed groups of ship-rocking time series in horizontal and vertical directions utilizing a Hilbert based method from statistical physics. Based on these results we could predict certain amount of the ship-rocking time series based on the current and the previous values. Our predictions are as accurate as the conventional methods from stochastic processes and provide a much wider prediction time range.
关键词： Ship-rocking Forecasting,Hilbert Transform
更新于2025-09-16 10:30:52
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Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting
摘要： In view of the problems of insu?cient real-time measurements in active distribution networks, a state estimation method for active distribution networks is proposed based on the forecasting of photovoltaic (PV) power generation. First, the extreme learning machine (ELM) enhanced by the genetic algorithm (GA) is used to forecast the PV power generation. Second, the Gaussian mixture model (GMM) is used to model the forecasting error. The weighted mean of the forecasting error is used to correct the forecasting value of the PV power generation, and the weighted variance of the forecasting error is used as the basis for setting the pseudo measurement weight. Finally, the real-time measurements collected by the supervisory control and data acquisition (SCADA) system, the forecasted pseudo measurements, and the virtual measurements are used to estimate the state of the active distribution network using the weighted least square (WLS) algorithm. Through simulations in the IEEE 33-bus system, it is shown that the proposed model provides accurate and reliable pseudo measurements for the active distribution network, improves the redundancy of the system, and thus further improves the accuracy of the state estimation and the capability of detecting and identifying bad data in active distribution systems without adding measurement devices.
关键词： Gaussian mixture model,Bad data,Forecasting of photovoltaic power generation,Active distribution system,State estimation,Pseudo measurement
更新于2025-09-16 10:30:52