Comparative study of PV power forecast using parametric and nonparametric PV models

Almeida, Marcelo Pinho; Muñoz, Margarita; Parra, I. de la; David, Mathieu

doi:10.1016/j.solener.2017.07.032

Cited by 55 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, due to cancellation of errors, the overall error of PV power forecasting is almost always smaller than the algebraic sum of errors in each step (forecasting, separation, transposition, and irradiance-to-power conversion). It is therefore of interest to study such error propagation, see Urraca et al (2018); Almeida et al (2017) for some preliminary findings. With the verification framework discussed earlier, the Murphy-Winkler factorizations could be applied to each of the steps, and thus provide new insights to solar engineers.…”

Section: Irradiance To Pv Power Output Conversion Issuesmentioning

confidence: 99%

Verification of deterministic solar forecasts

et al. 2020

View full text Add to dashboard Cite

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.

show abstract

Section: Irradiance To Pv Power Output Conversion Issuesmentioning

confidence: 99%

Verification of deterministic solar forecasts

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Up to now, the most common way to estimate the behavior of PV systems has been the use of classic models based on the physical process of the solar cell to define the analytical equation to obtain its electrical parameter [ 8 ]. There are many of these models with very different approaches, difficulty levels and results [ 4 , 9 , 10 , 11 ]. The main objective of these tools is to nowcast the electrical energy generated by the cells and also by the PV system.…”

Section: Related Workmentioning

confidence: 99%

A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy

Almonacid-Olleros

Almonacid

Fernández-Carrasco

et al. 2020

Sensors

View full text Add to dashboard Cite

The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance.

show abstract

“…The most usual way to forecast the behaviour of the PV systems is, up to now, the use of classical models based on the physic process of the solar cell to define the analytical equation to get its electric parameter [5]. There are a lot of these models with very different approaches, difficulty level and accuracy results [6][7][8][9]. The main objective of these tools is to forecast the electrical energy generated by the cells and also by the PV systems at all.…”

Section: Related Workmentioning

confidence: 99%

Opera.DL: Deep Learning Modelling for Photovoltaic System Monitoring

Almonacid-Olleros

Almonacid

Fernández-Carrasco

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

View full text Add to dashboard Cite

In this paper we present Deep Learning (DL) modelling to forecast the behaviour and energy production of a photovoltaic (PV) system. Using deep learning models rather than following the classical way (analytical models of PV systems) presents an outstanding advantage: context-aware learning for PV systems, which is independent of the deployment and configuration parameters of the PV system, its location and environmental conditions. These deep learning models were developed within the Ópera Digital Platform using the data of the UniVer Project, which is a standard PV system that was in place for the last twenty years in the Campus of the University of Jaén (Spain). From the obtained results, we conclude that the combination of CNN and LSTM is an encouraging model to forecast the behaviour of PV systems, even improving the results from the standard analytical model.

show abstract

Comparative study of PV power forecast using parametric and nonparametric PV models

Cited by 55 publications

References 30 publications

Verification of deterministic solar forecasts

Verification of deterministic solar forecasts

A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy

Opera.DL: Deep Learning Modelling for Photovoltaic System Monitoring

Contact Info

Product

Resources

About