Using neural networks to model and forecast solar PV power generation at Isle of Eigg

Anderson, William W.; Yakimenko, Oleg A.

doi:10.1109/cpe.2018.8372522

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…Exogenous Inputs (NARXNN) NARXNN is a typical dynamic neural network which consists of static neurons and output feedback [2]. The standard architecture of NARXNN is parallel, according to which the model output is directly fed back into the model input.…”

Section: F Nonlinear Autoregressive Neural Network Withmentioning

confidence: 99%

“…Artificial Neural Networks (ANN) are widely used in this context; some of the recent forecasting methods are discussed in the following. A Back-Propagation Neural Network (BPNN) is adopted in [1] for 24 hours ahead solar power forecasting, while the study in [2] explores a Non-linear Auto Regressive Neural Network with Exogenous Inputs (NARXNN) to predict the PV generation power at a standalone micro grid on a remote island. The authors of [3] achieve a 72-hour ahead PV power forecasting using an Elman Neural Network (ENN) and [4] presents a Generalized Regression Neural Network (GRNN) combined with Wavelet Transform (WT) for short-term PV power forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Algorithms in Forecasting of Photovoltaic Power Generation

Batzelis

Pal

2019

2019 International Conference on Smart Energy Systems and Technologies (SEST)

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Due to the intrinsic intermittency and stochastic nature of solar power, accurate forecasting of the photovoltaic (PV) generation is crucial for the operation and planning of PVintensive power systems. Several PV forecasting methods based on machine learning algorithms have recently emerged, but a complete assessment of their performance on a common framework is still missing from the literature. In this paper, a comprehensive comparative analysis is performed, evaluating ten recent neural networks and intelligent algorithms of the literature in short-term PV forecasting. All methods are properly fine-tuned and assessed on a one-year dataset of a 406 MWp PV plant in the UK. Furthermore, a new hybrid prediction strategy is proposed and evaluated, derived as an aggregation of the most well-performing forecasting models. Simulation results in MATLAB show that the season of the year affects the accuracy of all methods, the proposed hybrid one performing most favorably overall.

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Section: F Nonlinear Autoregressive Neural Network Withmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms in Forecasting of Photovoltaic Power Generation

Batzelis

Pal

2019

2019 International Conference on Smart Energy Systems and Technologies (SEST)

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“…Artificial Neural Networks (ANN) is considered the most successful method for PV forecasting and is used widely thanks to its ability to model non-linear, complex, and dynamic processes [10]. For example, Andersson & Yakimenko [14] explored a Non-linear Auto-Regressive with Exogenous Inputs Neural Network (NARXNN) to forecast the PV output for a microgrid. Liu, Liu, Sun, Li & Wennersten [15] adopted a Backpropagation Neural Network (BPNN) for 24-hour-ahead solar PV prediction.…”

Section: Introduction and Problematicmentioning

confidence: 99%

Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance

Frederiksen

Cai

2022

Applied Energy

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“…With regard to the different machine learning techniques used in PV installations and batteries, most works are centered in Recurrent Neural Networks (RNN). In [20] the model Nonlinear AutoRegressive eXogenous models (NARX) is used to predict the output power of the panels and a studio of different combinations of its inputs is made. Other RNN used is Long-Short Term Memory (LSTM) networks, for example in [21] this network is combined with statistical methods to forecast PV power with an horizon of 24 hours.…”

Section: Introductionmentioning

confidence: 99%