Analysis and Development Potential of Predictive Models for Energy Flows of Autonomous Hybrid Energy Systems-Reference-Cited by-同舟云学术

Analysis and Development Potential of Predictive Models for Energy Flows of Autonomous Hybrid Energy Systems

Published:2021-11-01 Issue:1 Volume:2096 Page:012024
ISSN:1742-6588
Container-title:Journal of Physics: Conference Series
language:
Short-container-title:J. Phys.: Conf. Ser.

Author:

Berdonosov V D,Vasilev G V,Zhivotova A A

Abstract

Abstract The article is devoted to the analysis and development potential for predictive models of energy flows of autonomous hybrid energy systems. The article considers the results of the analysis in the form of a morphological table and TRIZ–evolutionary map. The research defined the most promising in predicting energy flows are hybrid models that include more than one architecture. For example, DCNN + LSTM or MLP + LS–SVR. The authors intend to continue research in the direction of creating predictive models of wind energy flows for autonomous hybrid energy systems.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Link

https://iopscience.iop.org/article/10.1088/1742-6596/2096/1/012024/pdf

Reference32 articles.

1. Decentralized optimal control of a microgrid with solar PV, BESS and thermostatically controlled loads;Zhuo;Energies,2019

2. A data–driven short–term forecasting model for offshore wind speed prediction based on computational intelligence;Panapakidis;Electronics (Switzerland),2019

3. Multiple–input deep convolutional neural network model for short–term photovoltaic power forecasting;Huang;IEEE Access,2019

4. Next–day prediction of hourly solar irradiance using local weather forecasts and LSTM trained with non–local data;Jeon;Energies,2020

5. Performance improvement of artificial neural network model in short–term forecasting of wind farm power output;Medina;J. of Modern Power Systems and Clean Energy,2020