The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Time series analysis of the electricity demand in a residential building in South Korea

주거용 건물의 전력 사용량에 대한 시계열 분석 및 예측

  • Park, Kyeongmi (Department of Statistics, Duksung Women's University) ;
  • Kim, Jaehee (Department of Statistics, Duksung Women's University)
  • 박경미 (덕성여자대학교 수학 및 통계학과) ;
  • 김재희 (덕성여자대학교 수학 및 통계학과)
  • Received : 2019.01.28
  • Accepted : 2019.04.22
  • Published : 2019.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Predicting how much energy to use is an important issue in society. However, it is more difficult to capture the usage characteristics of residential buildings than other buildings. This paper provides time series analysis methods for electricity consumption in a residential building. Temperature is closely related to electricity demand. An error correction model, which is a method of adjusting the error with time, is applied when a cointegration relation is established between variables. Therefore, we analyze data via ECMs with consideration of the temperature effect.

얼마나 많은 에너지를 사용하느냐에 대한 예측은 사회에서 중요한 이슈이다. 특히 주거 건물은 건물의 특성상 다른 건물에 비해 예측하기 힘들다. 본 논문에서는 주거용 건물의 전력 사용량에 대한 시계열 분석의 방법들을 설명하고자 한다. 일반적으로 온도는 전력 사용량과 밀접한 관련이 있다고 알려져 있다. 변수들 사이에 공적분 관계가 존재한다면, 시간에 따른 오차를 조정하는 방법인 오차수정모형을 적용한다. 전력 사용량과 온도를 포함한 변수들 사이에 공적분 관계가 있음을 보이고, 새로운 온도 반응 함수를 정의하여 온도 효과를 고려한 오차수정모형을 적용하고자 한다.

Keywords

GCGHDE_2019_v32n3_405_f0001.png 이미지

Figure 4.1. Daily electricity demand of L apartment from 2015 to 2016(KWh).

GCGHDE_2019_v32n3_405_f0002.png 이미지

Figure 4.2. Electricity demand comparison between 2015 and 2016.

GCGHDE_2019_v32n3_405_f0003.png 이미지

Figure 4.3. Temperature response function.

GCGHDE_2019_v32n3_405_f0004.png 이미지

Figure 4.4. ACF and PACF of the transformed data. ACF = autocorrelation function; PACF = partial ACF.

GCGHDE_2019_v32n3_405_f0005.png 이미지

Figure 4.5. Comparison of 30-day forecasting via error correction model.

GCGHDE_2019_v32n3_405_f0006.png 이미지

Figure 4.6. Fit and 30-day forecasting via model 4.

GCGHDE_2019_v32n3_405_f0007.png 이미지

Figure 4.7. Residual plot of model 4.

GCGHDE_2019_v32n3_405_f0008.png 이미지

Figure 4.8. Q-Q plot of model 4.

Table 4.1. Augmented dickey-fuller (ADF) test

GCGHDE_2019_v32n3_405_t0001.png 이미지

Table 4.2. Parameter estimation of ARIMA model

GCGHDE_2019_v32n3_405_t0002.png 이미지

Table 4.3. Evaluation of ARIMA model

GCGHDE_2019_v32n3_405_t0003.png 이미지

Table 4.4. Parameter estimation of ARIMAX model

GCGHDE_2019_v32n3_405_t0004.png 이미지

Table 4.5. Evaluation of ARIMAX model

GCGHDE_2019_v32n3_405_t0005.png 이미지

Table 4.6. Parameter estimation of ARIMA + GARCH model

GCGHDE_2019_v32n3_405_t0006.png 이미지

Table 4.7. Evaluation of ARIMA + GARCH model

GCGHDE_2019_v32n3_405_t0007.png 이미지

Table 4.8. Augmented Dickey-Fuller test of electricity demand and temperature

GCGHDE_2019_v32n3_405_t0008.png 이미지

Table 4.9. Cointegration test of electricity demand and temperature

GCGHDE_2019_v32n3_405_t0009.png 이미지

Table 4.10. Granger causality test between electricity demand and temperature

GCGHDE_2019_v32n3_405_t0010.png 이미지

Table 4.11. Evaluation of error correction models

GCGHDE_2019_v32n3_405_t0011.png 이미지

Table 4.12. Comparison of fit of 30-day forecasting via error correction models

GCGHDE_2019_v32n3_405_t0012.png 이미지

Table 4.13. Durbin Watson (DW) test of Model 4 for autocorrelation analysis of residuals

GCGHDE_2019_v32n3_405_t0013.png 이미지

References

  1. Apergis, N. and Payne, J. E. (2009). Energy consumption and economic growth in Central America: evidence from a panel cointegration and error correction model, Energy Economics, 31, 211-216. https://doi.org/10.1016/j.eneco.2008.09.002
  2. Apergis, N. and Payne, J. E. (2010). Energy consumption and growth in South America: evidence from a panel error correction model, Energy Economics, 32, 1421-1426. https://doi.org/10.1016/j.eneco.2010.04.006
  3. Box, G. E. P. and Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, Journal of the American Statistical Association, 65, 1509-1526. https://doi.org/10.1080/01621459.1970.10481180
  4. Chang, Y. and Eduardo, M. C. (2003). Electricity demand analysis using cointegration and error-correction models with time varying parameters: the Mexican case (Working Papers 2003-08), Rice University, Department of Economics.
  5. Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74, 427-431. https://doi.org/10.1080/01621459.1979.10482531
  6. Dickey, D. A. and Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root, Econometrica, 49, 1057-1072. https://doi.org/10.2307/1912517
  7. Erdogdu, E. (2006). Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey, Energy Policy, 35, 1129-1146. https://doi.org/10.1016/j.enpol.2006.02.013
  8. Engle, R. F. and Granger, C. W. J. (1987). Co-integration and error correction: representation, estimation, and testing, Econometrica, 55, 251-276. https://doi.org/10.2307/1913236
  9. Glasure, Y. U. and Lee, A. R. (1997). Cointegration, error-correction, and the relationship between GDP and energy: the case of South Korea and Singapore, Resource and Energy Economics, 20, 17-25 . https://doi.org/10.1016/S0928-7655(96)00016-4
  10. Granger, C. W. J. (1981). Some Properties of time series data and their use in econometric model specification, Journal of Econometrics, 16, 121-130. https://doi.org/10.1016/0304-4076(81)90079-8
  11. Kumari, A. and Sharma, A. K. (2016). Analyzing the causal relations between electric power consumption and economic growth in India, The Electricity Journal, 29, 28-35. https://doi.org/10.1016/j.tej.2016.04.008
  12. Pao, H. T. (2006). Comparing linear and nonlinear forecasts for Taiwan's electricity consumption, Energy, 31, 2129-2141. https://doi.org/10.1016/j.energy.2005.08.010
  13. Park, D. and Yoon, S. (2017). Clustering and classification to characterize daily electricity demand, Journal of the Korean Data & Information Science Society, 28, 395-406. https://doi.org/10.7465/jkdi.2017.28.2.395
  14. Seo, H. C., Hong, W. H., and Nam, G. M. (2012). Characteristics of electric-power use in residential building by family composition and their income level, Journal of the Korean Housing Association, 23, 31-38.
  15. Shahbaz, M., Tang, C. F., and Shabbir, M. S. (2011). Electricity consumption and economic growth nexus in Portugal using cointegration and causality approaches, Energy Policy, 39, 3529-3536. https://doi.org/10.1016/j.enpol.2011.03.052
  16. Shahbaz, M. and Lean, H. H. (2012). The dynamics of electricity consumption and economic growth: a revisit study of their causality in Pakistan, Energy, 39, 146-153. https://doi.org/10.1016/j.energy.2012.01.048
  17. Shin, Y. and Yoon, S. (2016). Electricity forecasting model using specific time zone, Journal of the Korean Data & Information Science Society, 27, 275-284. https://doi.org/10.7465/jkdi.2016.27.2.275
  18. Yoon, S. and Choi, Y. (2015). Functional clustering for electricity demand data: a case study, Journal of the Korean Data & Information Science Society, 26, 885-894. https://doi.org/10.7465/jkdi.2015.26.4.885