A method for detailed, short-term energy yield forecasting of photovoltaic installations

Anagnostos, Dimitrios; Schmidt, Thomas; Cavadias, S.; Soudris, D.; Poortmans, Jef; Catthoor, Francky

doi:10.1016/j.renene.2018.06.058

Cited by 48 publications

(20 citation statements)

References 21 publications

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“…Deep learning-based models (ANN) are commonly used for intra-hour solar forecasts (Anagnostos et al, 2019) due to its ability for complex non-linear mappings (Inman et al, 2013). Multilayer perceptron (MLP) is one of the most established ANN structures and has been introduced to forecast intra-hour GHI, DNI, and power generation.…”

Section: Forecasts Based On Deep Learning Methodsmentioning

confidence: 99%

Intra-hour irradiance forecasting techniques for solar power integration: A review

Chu¹,

Coimbra

et al. 2021

iScience

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The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

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Section: Forecasts Based On Deep Learning Methodsmentioning

confidence: 99%

Intra-hour irradiance forecasting techniques for solar power integration: A review

Chu¹,

Coimbra

et al. 2021

iScience

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“…It has been proven that the PV power station equipped with energy storage can smooth the power fluctuation effectively [6,7]; especially, the energy storage system with high power density can effectively smooth the short-term and severe PV power fluctuation [8]. In Guo T., Liu Y., Zhao J., et al [9], a new robust dynamic wavelet-enabled method is proposed, which can optimize the wavelet parameters adaptively and adjust the state of charge (SOC) and depth of charge or discharge of the hybrid energy storage system (HESS) composed of supercapacitors and batteries so as to smooth the fluctuations of the output power.…”

Section: Introductionmentioning

confidence: 99%

An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing

Zhao

Chen

Chen³

et al. 2021

Energies

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With existing power prediction algorithms, it is difficult to satisfy the requirements for prediction accuracy and time when PV output power fluctuates sharply within seconds, so this paper proposes a high-precision and ultra-fast PV power prediction algorithm. Firstly, in order to shorten the optimization time and improve the optimization accuracy, the single-iteration Gray Wolf Optimization (SiGWO) method is used to simplify the iteration process of the hyperparameters of Least Squares Support Vector Machine (LSSVM), and then the hybrid local search algorithm composed of Iterative Local Search (ILS) and Self-adaptive Differential Evolution (SaDE) is used to improve the accuracy of hyperparameters, so as to achieve high-precision and ultra-fast PV power prediction. The power prediction model is established, and the proposed algorithm is applied in a test experiment which can complete the power prediction within 3 s, and the RMSE is only 0.44%. Finally, combined with the PV-storage advanced smoothing control strategy, it is verified that the performance of the proposed algorithm can satisfy the system’s requirements for prediction accuracy and time under the condition of power mutation in a PV power generation system.

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“…The application of machine learning techniques to the PV field has been explored before in the literature. For example, in [10], a set of NN is used to make a 15-minute forecast of the energy yield of the installation. Other studies such as [11] and [12] use more complex NN to make a generation of the installation prediction, with a prediction horizon up to 24 hours.…”

Section: Introductionmentioning

confidence: 99%

Energy Shortage Failure Prediction in Photovoltaic Standalone Installations by Using Machine Learning Techniques

et al. 2021

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A method for detailed, short-term energy yield forecasting of photovoltaic installations

Cited by 48 publications

References 21 publications

Intra-hour irradiance forecasting techniques for solar power integration: A review

Intra-hour irradiance forecasting techniques for solar power integration: A review

An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing

Energy Shortage Failure Prediction in Photovoltaic Standalone Installations by Using Machine Learning Techniques

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