e-ISSN 2231-8542
ISSN 1511-3701

Home / Regular Issue / JTAS Vol. 29 (4) Oct. 2021 / JST-2572-2021


Development of Micro-Spatial Electricity Load Forecasting Methodology Using Multivariate Analysis for Dynamic Area in Tangerang, Indonesia

Adri Senen, Christine Widyastuti, Oktaria Handayani and Perdana Putera

Pertanika Journal of Tropical Agricultural Science, Volume 29, Issue 4, October 2021


Keywords: Dynamic area, load forecasting, micro-spatial, multivariate

Published on: 29 October 2021

Dynamic population and land use significantly affect future energy demand. This paper proposes a suitable method to forecast load growth in a dynamic area in Tangerang, Indonesia. This research developed micro-spatial load forecasting, which can show load centres in microgrids, estimate the capacity and locate the distribution station precisely. Homogenous grouping implemented the method into clusters consisted of microgrids. It involves multivariate variables containing 12 electric and non-electric variables. Multivariate analysis is conducted by carrying out Principal Component Analysis (PCA) and Factor Analysis. The forecasting results can predict load growth, time, and location, which can later be implemented as the basis of a master electricity distribution plan because it provides an accurate long-term forecast.

  • Al Amin, M. A., & Hoque, M. A. (2019). Comparison of ARIMA and SVM for short-term load forecasting. In 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON) (pp. 1-6). IEEE Publishing.

  • Avazov, N., Liu, J., & Khoussainov, B. (2019). Periodic neural networks for multivariate time series analysis and forecasting. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE Publishing.

  • Babcock, C., Matney, J., Finley, A. O., Weiskittel, A., & Cook, B. D. (2013). Multivariate spatial regression models for predicting individual tree structure variables using LiDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(1), 6-14.

  • Carvallo, J. P., Larsen, P. H., Sanstad, A. H., & Goldman, C. A. (2016). Load forecasting in electric utility integrated resource planning. LBNL Publications.

  • El Kafazi, I., Bannari, R., Abouabdellah, A., Aboutafail, M. O., & Guerrero, J. M. (2017). Energy production: A comparison of forecasting methods using the polynomial curve fitting and linear regression. In 2017 International Renewable and Sustainable Energy Conference (IRSEC) (pp. 1-5). IEEE Publishing.

  • Fu, Q., Lai, R., Shan, Y., & Geng, X. (2018). A spatial forecasting method for photovoltaic power generation combined of improved similar historical days and dynamic weights allocation. In 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) (pp. 1195-1198). IEEE Publishing.

  • Jimenez, J., Pertuz, A., Quintero, C., & Montana, J. (2019). Multivariate statistical analysis based methodology for long-term demand forecasting. IEEE Latin America Transactions, 17(01), 93-101.

  • Kartikasari, M. D., & Prayogi, A. R. (2018). Demand forecasting of electricity in Indonesia with limited historical data. Journal of Physics: Conference Series, 974, Article 012040.

  • Kobylinski, P., Wierzbowski, M., & Piotrowski, K. (2020). High-resolution net load forecasting for micro-neighbourhoods with high penetration of renewable energy sources. International Journal of Electrical Power & Energy Systems, 117, Article 105635.

  • Lagaaij, A. (2018). Accelerating solar for decelerating climate change in time. In 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) (pp. 2392-2394). IEEE Publishing.

  • Mukhopadhyay, P., Mitra, G., Banerjee, S., & Mukherjee, G. (2017). Electricity load forecasting using fuzzy logic: Short term load forecasting factoring weather parameter. In 2017 7th International Conference on Power Systems (ICPS) (pp. 812-819). IEEE Publishing.

  • Raza, M. Q., Mithulananthan, N., Li, J., & Lee, K. Y. (2020). Multivariate ensemble forecast framework for demand prediction of anomalous days. IEEE Transactions on Sustainable Energy, 11(1), 27-36.

  • Sun, X., Ouyang, Z., & Yue, D. (2017). Short-term load forecasting based on multivariate linear regression. In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) (pp. 1-5). IEEE Publishing.

ISSN 1511-3701

e-ISSN 2231-8542

Article ID


Download Full Article PDF

Share this article

Recent Articles