Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

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Data for: Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Supplementary materials for "Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data" article

Data for: Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Supplementary materials for "Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data" article.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Infinite hidden Markov model for short-term solar irradiance forecasting

Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify. In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the “correct” complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations. Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler. Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon. For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material

Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

High resolution synthetic irradiance is of interest for theoretical studies such as grid integration of solar PV and battery storage analysis. Access to site-specific data is often limited to inadequate temporal resolutions for such application. A new model for producing synthetic solar global horizontal irradiance (GHI) time-series at up to 1-min resolution is presented as derived from >10-min input data. Briefly, it is a clustered-based method for daily clearness index distributions using Dirichlet process Gaussian mixture model (DPGMM). DPGMM is a nonparametric Bayesian (NPB) model indexed with an infinite-dimensional space of parameters. The key benefit of the NPB paradigm is the automatic adaptation to the correct complexity level and model size, suggesting a local adaptation of the model to all climatic conditions. A posterior inference using Markov chain Monte Carlo algorithm (namely Gibbs sampling) is applied. The model only requires a valid number of intraday data to construct daily distributions, then it can be applied worldwide. The synthetic GHI time series are validated against observed 1-min GHI data for four locations distributed throughout the world with different climatic conditions and significant geographic separation. Moreover, the presented method can generate data based on similar climatic conditions. A good fit between real and generated data is observed. We present an nRMSE ≤ 4% and nMBE < ±4% between generated and measured means at both daily and monthly scales for all sites. The agreement between the real and generated cumulative density distributions of six comparative variability metrics (defined in text) at four different sites is measured using the overlapping and the Kullback-Leibler coefficients, which are ≥ 75% and ≤ 10% respectively, in all cases. To ensure the reproducibility of the research presented in this paper, the methodology is freely available as an R-package downloadable from SolarClusGnr

Identifying small decentralized solar systems in aerial images using deep learning

Statistics on installed solar energy systems (SES) play a crucial role in the solar energy industry, providing valuable information for a wide range of stakeholders, such as policy makers, authorities, and financial evaluators. For example, grid operators rely on accurate data on photovoltaic penetration levels to ensure the quality and stability of the power supply. In this research, we present an automatic approach helping generate these statistics using deep learning and image processing techniques. Our proposed model is a machine learning approach that utilizes a specific architecture of convolutional neural networks (CNN) called the "U-net'' to detect SES from aerial images. We experimented different network settings to enhance the SES identification performance.In this study, the model was evaluated using two datasets from different locations, one from Sweden and one from Germany. Additionally, the model was trained and tested on a combination of both datasets. The impact of image resolution was also examined. The experimental results show that this architecture performs better than many recent CNN models that have been proposed in the literature for the task of SES identification from aerial images. To make it easy for others to replicate our findings, We have shared all the scripts, software, and dependencies required for running the model in this paper, along with instructions on how to use it in Appendix A

Dirichlet downscaling model for synthetic solar irradiance time series

10.1063/5.0028267Journal of Renewable and Sustainable Energy126063702-06370

Biological pre-hydrolysis and thermal pretreatment applied for anaerobic digestion improvement : Kinetic study and statistical variable selection

In the present study, two pretreatment methods (thermal pretreatment and biological pre-hydrolysis) were suggested for food waste (FW) with the aim to enhance biomass conversion and biogas production by anaerobic. The effects of thermal pretreatment (TP), including TP at 60°C and 80°C for 60 min, and TP at 100°C, 120°C and 140°C for 30 min, well as biological pre-hydrolysis (BPH) at 37°C, 55°C, 37°C followed by 55°C and 55°C followed by 37°C for 40 hour on anaerobic digestion performance of FW were evaluated in batch tests. Results were compared with untreated FW. The BPH and TP caused an increase in the soluble chemical oxygen demand and hydrolysis efficiency. The methane yield (MY) increased from 371.17 ml CH4/g VS for untreated FW to 471.95 ml CH4/ g VS. The maximal MY was recorded for BPH at 37°C for 20 h followed by 55°C for 20 h. The pretreatments increased the biogas production rate and reduced the lag phase. The most influential variables on the methane yield were investigated using three statistical methods: Principal component analysis, Mutual Information and R-squared. The results allowed a good modeling of the methane yield and minimized the overfitting effect. For reproduction and solid contribution to the field, we have attached to our article all the necessary material to reproduce the same statistical work as in the paper body