Digital Library[ Search Result ]
Design of Photovoltaic Power Generation Prediction Model with Recurrent Neural Network
Hanho Kim, Haesung Tak, Hwan-gue Cho
http://doi.org/10.5626/JOK.2019.46.6.506
The Smart Grid predicts the power generation amount of renewable energy and enables efficient power generation and consumption. Existing PV power generation prediction studies have rarely applied and compared recurrent neural network techniques that are superior to time series. Furthermore, in the reported studies, there is no consideration of the length of past data used for learning, leading to lowered prediction performance of the model. In this study, we used the embedded variable selection techniques to find the factors influencing PV power generation. Subsequently, experiments were carried out to insert various past data length into the recurrent neural networks (RNN, LSTM, GRU). We found the optimal prediction factors and designed a prediction model based on the outcomes of the experiments. The designed PV power generation prediction model shows better prediction performance compared to other factor settings. In addition, better performance based on the prediction rate is confirmed in the present study as compared with the existing researches.
Agent-Based Modeling and Simulation Methodology using Social-Level Characteristics: A Case Study on Self-Adaptive Smart Grid and Military Domain Systems using Tropos
Agent-based modeling and simulation (ABMS) is used to model of market and social phenomena by utilizing agents’ fine-grained behaviors and interactions that cannot be implemented in a conventional simulation. However, ABMS represents irrational agents and hinders the achievement of individual or overall goals since ABMS is based on agent-based software, which follows the principle of rationality at the knowledge level [1]. This problem was solved in the agent-based software engineering (ABSE) field by using behavior laws for the social level [2]. However, they still do not propose the specific development methodology for how to develop the social level in a systematic way. Therefore, in order to propose agent-based modeling and simulation methods that reflect the behavior laws of social level characteristics, our study used the Tropos that can combine ABSE and social behavior laws for the presentation of concrete tasks and deliverables for each development step by step. In addition, the proposed method will be specified through experiments with specific application examples and case studies on the self-adaptive smart grid and the military domain system.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr