Unsupervised clustering of battery waveforms in off-grid PV installations

Sanz-Gorrachategui, Iván; Pastor-Flores, Pablo; Guillen-Asensio, A.; Sevil, Jesús Sergio Artal; Bono-Nuez, Antonio; Martin-del-Brio, B.; Bernal-Ruíz, Carlos

doi:10.1109/ever48776.2020.9242942

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…With this tool all the days of the original training database are classified and tagged. For further details, [27] is included as a pointer. Afterward, five GPR experts are designed and trained, using for training days of their corresponding class.…”

Section: Gpr Based Multiple Expertsmentioning

confidence: 99%

Online voltage prediction using gaussian process regression for fault-tolerant photovoltaic standalone applications

Sanz-Alcaine

Sebastián

Sanz-Gorrachategui

et al. 2021

Neural Comput & Applic

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This paper presents a fault detection system for photovoltaic standalone applications based on Gaussian Process Regression (GPR). The installation is a communication repeater from the Confederación Hidrográfica del Ebro (CHE), public institution which manages the hydrographic system of Aragón, Spain. Therefore, fault-tolerance is a mandatory requirement, complex to fulfill since it depends on the meteorology, the state of the batteries and the power demand. To solve it, we propose an online voltage prediction solution where GPR is applied in a real and large dataset of two years to predict the behavior of the installation up to 48 hour. The dataset captures electrical and thermal measures of the lead-acid batteries which sustain the installation. In particular, the crucial aspect to avoid failures is to determine the voltage at the end of the night, so different GPR methods are studied. Firstly, the photovoltaic standalone installation is described, along with the dataset. Then, there is an overview of GPR, emphasizing in the key aspects to deal with real and large datasets. Besides, three online recursive multistep GPR model alternatives are tailored, justifying the selection of the hyperparameters: Regular GPR, Sparse GPR and Multiple Experts (ME) GPR. An exhaustive assessment is performed, validating the results with those obtained by Long Short-Term Memory (LSTM) and Nonlinear Autoregressive Exogenous Model (NARX) networks. A maximum error of 127 mV and 308 mV at the end of the night with Sparse and ME, respectively, corroborates GPR as a promising tool.

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Section: Gpr Based Multiple Expertsmentioning

confidence: 99%

Online voltage prediction using gaussian process regression for fault-tolerant photovoltaic standalone applications

Sanz-Alcaine

Sebastián

Sanz-Gorrachategui

et al. 2021

Neural Comput & Applic

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Analyzing Electric Vehicle Charging Behaviour Using Advanced Clustering Tools

da Veiga,

Ramos,

Corchado

et al. 2023

Lecture Notes in Networks and Systems

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Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms

Matrenin,

Khalyasmaa,

Gamaley

et al. 2023

Izv. vysh. ucheb

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Renewable energy sources (RES) are seen as a means of the fuel and energy complex carbon footprint reduction but the stochastic nature of generation complicates RES integration with electric power systems. Therefore, it is necessary to develop and improve methods for forecasting of the power plants generation using the energy of the sun, wind and water flows. One of the ways to improve the accuracy of forecast models is a deep analysis of meteorological conditions as the main factor affecting the power generation. In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. Then, for each cluster, its own machine learning model was trained for generation forecasting and the k-nearest neighbours algorithm was built to attribute the current conditions at the model operation stage to one of the formed clusters. The study was conducted on hourly meteorological data for the period from 1985 to 2021. A feature of the approach is the clustering of weather conditions on hourly rather than daily intervals. As a result, the mean absolute percentage error of forecasting is reduced significantly, depending on the prediction model used. For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %.

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Unsupervised clustering of battery waveforms in off-grid PV installations

Cited by 3 publications

References 15 publications

Online voltage prediction using gaussian process regression for fault-tolerant photovoltaic standalone applications

Online voltage prediction using gaussian process regression for fault-tolerant photovoltaic standalone applications

Analyzing Electric Vehicle Charging Behaviour Using Advanced Clustering Tools

Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms

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