Spatial-temporal solar power forecast through use of Gaussian Conditional Random Fields

Zhang, Bei; Dehghanian, Payman; Kezunovic, Mladen

doi:10.1109/pesgm.2016.7741503

Cited by 15 publications

(10 citation statements)

References 20 publications

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“…In contrast to day-ahead forecasting, very few spatial-temporal models for short-term probabilistic forecasting feature in the literature. These models are based on regression trees [12], the kNN method [13], the combination of a vectorial autoregressive model and gradient boosting [41], multivariate predictive distributions, [42] and Gaussian random fields [43].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Agoua

Girard

Kariniotakis

2019

IEEE Trans. Sustain. Energy

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Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose a probabilistic spatio-temporal model for the PV power production that exploits production information from neighboring plants. The model provides the complete future probability density function of PV production for very short-term horizons (0-6 hours). The method is based on quantile regression and a L1 penalization technique for automatic selection of the input variables. The proposed modeling chain is simple, making the model fast and scalable to direct on-line application. The performance of the proposed approach is evaluated using a real-world test case, with a high number of geographically distributed PV installations and by comparison with state-ofthe-art probabilistic methods.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Agoua

Girard

Kariniotakis

2019

IEEE Trans. Sustain. Energy

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“…After obtaining the results, the final solar power was compared with persistence and ARX model after several experiments with and without pre-processing of data. It was concluded by the RMSE that GCRF has attained the forecasting estimation accurately as compare to persistence and ARX [18].…”

Section: Literature Reviewmentioning

confidence: 97%

Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

Babbar¹,

Lau²,

Thang³

2021

IJACSA

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The usage of renewable energy sources has increased manifolds in terms of electric utilities. From other nonconventional sources, solar energy has been seen as a promising and convenient source used around the globe. In terms of meeting the daily requirements, solar energy has huge potential to fulfil the world's demand. However, firstly the characteristic of solar energy is very unpredictable and intermittent due to variation of weather. Secondly, the optimization and the planning of smart grid effect the operation of PV system. Thus, prediction on the long horizon is needed to address this problem. Nevertheless, long term forecasting of solar power generation is deliberated as a challenging problem. Therefore, this paper proposes a 10 day ahead solar power forecasting using combination of linear and non-linear machine learning models. At first, the outputs are generated from Recurrent Neural Network (RNN), Support Vector Machine (SVM) and Autoregressive with exogenous variable (ARX). Later on, these three outputs are combined and are made as a strong classifier with the Adaptive boost (Adaboost) algorithm. The simulations were conducted on the data obtained from real PV plant. By the experimental results and discussion, it was endogenously concluded that the combination of all techniques with the Adaboost have increased the performances and showing the high accuracy as compare to the individual machine learning models. The hybrid Adaboost shows %MAPE 8.88, which proven high accuracy. While on the other hand, for the individual technique, RNN shows 10.88, SVM reveals 11.78 and ARX gives 13.00 of percentage MAPE. The improvement proves that combination of techniques performs better than individual models and proclaims the high accuracy.

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“…The spatial and temporal correlations of different solar generation plants are implemented in a probabilistic forecasting method based on the Gaussian Conditional Random Fields (GCRF). The forecasts reach more accurate results than the common models such as the persistent model and the autoregressive model [7]. The power forecasts for PV plants in the South of Italy were performed by a Compressive Spatio-Temporal Forecasting (CSTF) algorithm, using data from different meteorological stations [8].…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Forecasting Method for Dispersed Pv Plants

Malvoni¹,

Hatziargyriou²

2021

The 12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2020)

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The study proposes a novel approach based on the three-dimensional wavelet transform (3D-DWT) and the Least Square Support Vector Machines (LS-SVM) to forecast the power output of spread PV systems. The proposed forecasting method can capture the correlations of distributed PV installations providing more accurate predictions. The output power of different PV plants in Rhodes, Greece is applied to forecast the power generation of each PV system at 1, 12 and 24 hours-ahead time horizons. The accuracy is also expressed by the error metrics.

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Spatial-temporal solar power forecast through use of Gaussian Conditional Random Fields

Cited by 15 publications

References 20 publications

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

A Spatio-Temporal Forecasting Method for Dispersed Pv Plants

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