The Prediction and Analysis of the Power Energy Time Series by Using the Elman Recurrent Neural Network

• 영문명: 엘만 순환 신경망을 사용한 전력 에너지 시계열의 예측 및 분석
• 발행기관: 한국산업경영시스템학회
• 저자명: Chang-Yong Lee(이창용) Jinho Kim(김진호)
• 간행물 정보: 『산업경영시스템학회지』제41권 제1호, 84~93쪽, 전체 10쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2018.03.30

영문 초록

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of “context units” in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power con-sumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

목차

1. 서 론

2. 전력 시계열의 비선형 상관관계

3. 순환 신경망을 사용한 시계열 데이터 예측

4. 결론 및 요약

키워드

Time Series Prediction Elman Recurrent Neural Network Power Energy Consumption Forecast Volatility