Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems

This study addresses the use of GPR techniques for VRLA battery voltage prediction purposes in PV off-grid systems. The goal is to know whether the system is able to endure a predictable power consumption pattern without running out of energy. Two approaches are considered: sample based prediction and pattern-based forecasting

Energy shortage failure prediction in photovoltaic standalone installations by using machine learning techniques

The use of energy storage systems in standalone photovoltaic installations is essential to supply energy demands, independently of solar generation. Accurate prediction of the battery state is critical for the safe, durable, and reliable operation of systems in this type of installations. In this study, an installation located in the area of Aragon (Spain) has been considered. Two methods, based on different types of Recurrent Neural Networks (RNN), are proposed to predict the battery voltage of the installation two days ahead. Specifically, the Nonlinear Auto Regressive with Exogenous Input (NARX) network and the Long Short-Term Memory (LSTM) network are studied and compared. The implemented algorithms process battery voltage, temperature and current waveforms; and rely on the selection of different future scenarios based on weather forecasting to estimate the future voltage of the battery. The proposed methodology is capable of predicting the voltage with a Root Mean Squared Error (RMSE) error of 1.2 V for batteries of 48 V, in critical situations where the installation is running out of energy. The study contributes to the ongoing research of developing preventive control systems that help reduce costs and improve the performance of remote energy storage systems based on renewable energies with a positive outcome

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

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 Confederacion Hidrografica del Ebro (CHE), public institution which manages the hydrographic system of Aragon, 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

Procesos de regresión gaussiana: Estudio de métodos Sparse para la predicción de tensión futura en equipos de comunicaciones

Este Trabajo Fin de Grado forma parte de la línea Smart del proyecto de investigación "Battery life Extensor" (BATT-Ex), cuyos principales objetivos son proporcionar información relevante sobre la batería en sistemas de telecocomunicaciones y tomar decisiones inteligentes en base al conocimiento de su estado actual. En los últimos años las baterías han recibido mucha atención debido a las redes de comunicaciones y a la aparición de los Vehículos Híbridos (HEVs) y los Vehículos Eléctricos (EVs). Esto ha llevado a que centros de investigación como el Mitsubishi Electric Research Laboratories (MERL) en Boston o empresas como la Sociedad Ibérica de Construcciones Eléctricas (SICE) hayan querido profundizar en su investigación y colaborar en el siguiente trabajo de investigación.Con el presente trabajo se pretende mejorar las técnicas de estimación futura de tensión en baterías por procesado de la señal de tensión-corriente, en instalaciones aisladas de comunicaciones. Para ello se han utilizado técnicas de Inteligencia Artificial. En particular, se ha estudiado el uso de la regresión con procesos gaussianos (GPR) como herramienta de predicción y sus diferentes variantes Sparse para reducir la complejidad computacional del algoritmo.Para llevar a cabo la estimación se necesita información sobre las variables de interés. Para ello, se ha utilizado la base de datos de un sistema fotovoltaico de gran potencia ubicado en el monte del Monasterio de Sigena la cual contiene 10 años de datos recabados cada 15 minutos de parámetros como tensión, corriente o temperatura.De forma más concreta, se ha evaluado el GPR en su enunciado clásico, el método FITC Sparse y una variante con múltiples expertos GPR comparando los resultados de estimación de tensión aportando el perfil de corriente o temperatura futura.<br /