A robust forecasting framework based on the Kalman filtering approach with a twofold parameter tuning procedure: Application to solar and photovoltaic prediction

Soubdhan, Ted; Ndong, Joseph; Ould-Baba, Hanany; Do, Minh

doi:10.1016/j.solener.2016.02.036

Cited by 52 publications

(30 citation statements)

References 14 publications

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“…As is commonly done in the literature (Huang et al, 2014;Marquez and Coimbra, 2013;Rana et al, 2016;Soubdhan et al, 2016;Voyant et al, 2017), we use the assumption that the future power will remain unchanged over the forecasting horizon (∆p t 0 = 0) as a baseline. This is commonly known as the "persistence" model.…”

Section: Error Metricsmentioning

confidence: 99%

Deep photovoltaic nowcasting

et al. 2018

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Predicting the short-term power output of a photovoltaic panel is an important task for the efficient management of smart grids. Short-term forecasting at the minute scale, also known as nowcasting, can benefit from sky images captured by regular cameras and installed close to the solar panel. However, estimating the weather conditions from these images-sun intensity, cloud appearance and movement, etc.-is a very challenging task that the community has yet to solve with traditional computer vision techniques. In this work, we propose to learn the relationship between sky appearance and the future photovoltaic power output using deep learning. We train several variants of convolutional neural networks which take historical photovoltaic power values and sky images as input and estimate photovoltaic power in a very short term future. In particular, we compare three different architectures based on: a multi-layer perceptron (MLP), a convolutional neural network (CNN), and a long short term memory (LSTM) module. We evaluate our approach quantitatively on a dataset of photovoltaic power values and corresponding images gathered in Kyoto, Japan. Our experiments reveal that the MLP network, already used similarly in previous work, achieves an RMSE skill score of 7% over the commonly-used persistence baseline on the 1-minute future photovoltaic power prediction task. Our CNN-based network improves upon this with a 12% skill score. In contrast, our LSTM-based model, which can learn the temporal dependencies in the data, achieves a 21% RMSE skill score, thus outperforming all other approaches.

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Section: Error Metricsmentioning

confidence: 99%

Deep photovoltaic nowcasting

et al. 2018

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“…• Statistical learning based methods-work best for the intra-hour forecast horizons, but can also be applied for longer forecasting, up to 2 or 3 h, when combined with other methods [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

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“…While ARMA model developed with Expectation Maximisation algorithm is robust and reliable in short-term forecasting for heterogeneous dataset [211], David et al [212] improved the prediction reliability of recursive ARMA model through its association with GARCH model, wherein uncertainties in solar forecasting were captured. Besides, Benmouiza and Cheknane [213] hybridised ARMA and NAR models for simultaneous capturing of linear and non-linear solar irradiance time series patterns.…”

Section: Referencesmentioning

confidence: 99%