The Paris Agreement calls for maintaining a global temperature less than 2°C above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5°C. To realize this objective and promote a low-carbon society, and because energy production and use is the largest source of global greenhouse-gas (GHG) emissions, it is important to efficiently manage energy demand and supply systems. This, in turn, requires theoretical and practical research and innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-series analysis, and the application of these technologies at urban and national scales. Further, because developing countries contribute increasing shares of domestic energy consumption, it is important to consider the application of such innovations in these areas. Motivated by the mandates set out in global agreements on climate change and low-carbon societies, this paper focuses on the development of a smart energy monitoring system (SEMS) and its deployment in households and public and commercial sectors in Bogor, Indonesia. An electricity demand prediction model is developed for each device using the Auto-Regression eXogenous model. The real-time SEMS data and time-series clustering to explore similarities in electricity consumption patterns between monitored units, such as residential, public, and commercial buildings, in Bogor is, then, used. These clusters are evaluated using peak demand and Ramadan term characteristics. The resulting energy-prediction models can be used for low-carbon planning.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Maki S, Ashina S, Fujii M, et al. Installing energy monitoring system for consumer sector in Indonesia and energy use prediction by multiple-time series modelling. Journal of Japan Society of Civil Engineers, Ser.G (Environmental Research), 2017, 73(6): II_35–II_43 (in Japanese)
Hargreaves T, Nye M, Burgess J. Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term. Energy Policy, 2013, 52: 126–134
Kindaichi S, Nishina D, Murakawa S, Tanaka T, Horioka K. Energy demand and the factor analysis in a cafe in the campus. Journal of Environmental Engineering, 2014, 79(696): 191–199
Kawano H, Yamada S, Abe H, et al. An electricity demand forecasting method for office buildings using a small data approach. Information Processing Society of Japan Consumer Device & System, 2014, 4(2): 1–9 (in Japanese)
Kondo S, Nobayasi M, Hokoi S. Forecasting model for electricity demand in residential house based on time series analysis. Journal of Japan Society of Energy and Resources, 2016, 37(1): 34–42 (in Japanese)
Ma J, Qin J, Salsbury T, Xu P. Demand reduction in building energy systems based on economic model predictive control. Chemical Engineering Science, 2012, 67(1): 92–100
Utma A, Gheewala S H. Indonesian residential high rise buildings: a life cycle energy assessment. Energy and Buildings, 2009, 41(11): 1263–1268
Permana A S, Perera R, Kumar S. Understanding energy consumption pattern of households in different urban development forms: a comparative study in Bandung City, Indonesia. Energy Policy, 2008, 36(11): 4287–4297
Chou J S, Gusti Ayu Novi Yutami I. Smart meter adoption and development strategy for residential buildings in Indonesia. Applied Energy, 2014, 128: 336–349
Sakoe H, Chiba S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978, 26(1): 43–49
Nakamoto K, Yamada Y, Suzuki E. Fast clustering for time-series data with average-time-sequence-vector generation based on dynamic time warping. Transactions of the Japanese Society for Artificial Intelligence, 2003, 18(3C): 144–152 (in Japanese)
The Energy Data and Modelling Center, Japan. EDMC Handbook of Japan’s & World Energy & Economic Statistics 2016. The Energy Conservation Center, Japan, 2016 (in Japanese)
Iwata G. Statistical Method for Economic Analysis, 2nd ed. Toyo Keizai Inc., 1983 (in Japanese)
Newey W, West K. A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix. Econometrica, 1987, 55(3): 703–708
Tomikoshi D, Ikaga T, Kawakubo S, et al. Development of a suggestion tool for energy-saving actions based on the analysis of residents’ behaviors and energy demand. AIJ Journal of Technology and Design, 2013, 19(42): 655–660 (in Japanese)
Agency for Natural Resources and Energy, Japan. Guidelines for trading negawatts. 2017–07, https://doi.org/www.meti.go.jp/press/2016/09/20160901003/20160901003-1.pdf (in Japanese)
This work was performed under the MOEJ “Innovative Modeling and Monitoring Research toward Low Carbon Society and Eco-Cities and Regions” project.
About this article
Cite this article
Maki, S., Ashina, S., Fujii, M. et al. Employing electricity-consumption monitoring systems and integrative time-series analysis models: A case study in Bogor, Indonesia. Front. Energy 12, 426–439 (2018). https://doi.org/10.1007/s11708-018-0560-4
- electricity monitoring
- electricity demand prediction
- multiple-variable time-series modeling
- time-series cluster analysis