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[IEEE 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Busan, Korea (South) (2020.2.19-2020.2.22)] 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) - Normalized Residue Analysis for Deep Learning Based Probabilistic Forecasting of Photovoltaic Generations
摘要： In this study, probabilistic forecasting schemes of day-ahead photovoltaic (PV) generations are investigated with the auto-regressive recurrent neural network model named DeepAR, and are evaluated based on the normalized residues. For PV generations, probabilistic outcomes should be helpful for efficient grid managements to account uncertainties such as sudden changes in the local weather. The tightness of the prediction interval for local PV generations is investigated with DeepAR models with varying input data like the local weather forecasts of the day and historical records of the PV generations. For performance measure, normalized residue with the mean and standard deviation of the predicted traces is compared to the standard normal distribution. For evaluation, local PV generation data captured at Hadong, Korea is tested by the DeepAR models with optional input of local weather forecasts data. The evaluation results of the PV generation tests show that the local weather data provides extra tightness of the prediction interval with the normalized residues close to the standard normal distribution.
关键词： standard score,probabilistic forecasting,photovoltaic system
更新于2025-09-23 15:19:57
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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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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