Analysis of Autoregressive Predictive Models and Artificial Neural Networks for Irradiance Estimation

Mauledoux, Mauricio; Sánchez, Oscar Fernando Avilés; Mejía-Ruda, Edilberto

doi:10.17485/ijst/2016/v9i38/86214

Cited by 3 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [34], the authors used artificial neural networks and autoregressive techniques in order to obtain a 24-h estimation of the solar irradiance. The authors used a meteorological dataset containing hourly records over a 5-years period for developing nonlinear autoregressive, nonlinear autoregressive with exogenous inputs, auto regressive moving average with exogenous terms, neural network fitting models in order to obtain the most accurate prediction model as to optimize the direct-current microgrid functioning.…”

Section: Introductionmentioning

confidence: 99%

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

et al. 2017

View full text Add to dashboard Cite

This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

show abstract

Section: Introductionmentioning

confidence: 99%

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

et al. 2017

View full text Add to dashboard Cite

show abstract

Link-level Travel Time Prediction Using Artificial Neural Network Models

Mane

Pulugurtha

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Modeling and Simulation of a DC Micro-Grid with a Model Predictive Controller

Prada

Caldas

Mejía-Ruda

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

This paper presents the modeling strategies of a micro-grid to control the power supply of a battery bank by adopting a Model Predictive Controller (MPC). The grid was sized to light two tennis courts at a university sports complex, which is not connected to the national power grid and thus must be a stand-alone setup. The paper starts with an introduction that defines the statement of purpose and the state of the art. Then continue with the power generators and storage modeling: photovoltaic (PV) modules, wind turbine, buck converter (takes power from the rectified national grid) and the battery bank. The MPC was designed to effectively manage the energy supplied to the batteries, depending on the state of charge, hence the controller output is the signal used to regulate the charging current. The data used for prediction is the meteorological measures taken during three years using an in-situ weather station that collected irradiance, wind speed and direction, temperature, and pressure. Finally, as the entire control system was simulated step by step using MATLAB/Simulink, the components and systems behavior graphs are shown to lead to analysis and conclusion remarks.

show abstract

Analysis of Autoregressive Predictive Models and Artificial Neural Networks for Irradiance Estimation

Cited by 3 publications

References 24 publications

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

Link-level Travel Time Prediction Using Artificial Neural Network Models

Modeling and Simulation of a DC Micro-Grid with a Model Predictive Controller

Contact Info

Product

Resources

About