L1 Optimization for Sparse Structure Machine Learning Based Electricity Demand Prediction

  • Dinh Hoa NguyenEmail author
Part of the Springer Optimization and Its Applications book series (SOIA, volume 152)


This chapter presents a study on L1 optimization for the problem of electricity demand prediction based on machine learning. This electricity demand prediction is very important for balancing the power supply and demand in smart power grids, a critical infrastructure in smart societies, where the energy consumption increases every year. Due to its robustness to outliers, L1 optimization is suitable to deal with challenges posed by the uncertainties on weather forecast, consumer behaviors, and renewable generation. Therefore, L1 optimization will be utilized in this research for machine learning techniques, which are based on artificial neural networks (ANNs), to cope with the nonlinearity and uncertainty of demand curves. In addition, two approaches, namely L2 and alternating direction method of multiplier (ADMM), will be used to solve the L1 optimization problem and their performances will be compared to find out which one is better. Test cases for realistic weather and electricity consumption data in Tokyo will be introduced to demonstrate the efficiency of the employed optimization approaches.


Electric demand prediction Machine learning Radial basis function neural network L1 optimization Alternating direction method of multipliers 



The author would like to send his thanks to the student, Mr. Nguyen Anh Tung, for his helps on the data collection and simulation; and to Prof. Kei Hirose at Institute of Mathematics for Industry (IMI), Kyushu University, for his fruitful discussions on this research.


  1. 1.
    Yao, R., Steemers, K.: A method of formulating energy load profile for domestic building in the UK. Energy Build. 37, 663–671 (2005)CrossRefGoogle Scholar
  2. 2.
    Kramer, R.P., van Schijndel, A.W.M., Schellen, H.L.: The importance of integrally simulating the building, HVAC and control systems, and occupants impact for energy predictions of buildings including temperature and humidity control: validated case study museum Hermitage Amsterdam. J. Build. Perform. Simul. 10, 272–293 (2017)CrossRefGoogle Scholar
  3. 3.
    Ozawa, A., Furusato, R., Yoshida, Y.: Determining the relationship between a households lifestyle and its electricity consumption in Japan by analyzing measured electric load profiles. Energy Build. 119, 200–210 (2016)CrossRefGoogle Scholar
  4. 4.
    Son, H., Kim, C.: Short-term forecasting of electricity demand for the residential sector using weather and social variables. Resour. Conserv. Recycl. 123, 200–207 (2017)CrossRefGoogle Scholar
  5. 5.
    McLoughlin, F., Duffy, A., Conlon, M.: Evaluation of time series techniques to characterise domestic electricity demand. Energy 50, 120–130 (2013)CrossRefGoogle Scholar
  6. 6.
    Gunay, M.E.: Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: case of Turkey. Energy Policy 90, 92–101 (2016)CrossRefGoogle Scholar
  7. 7.
    Bianco, V., Manca, O., Nardini, S.: Linear regression models to forecast electricity consumption in Italy. Energy Sources Part B Econ. Plan. Policy 8, 86–93 (2013)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Okaeme, C.C., Mishra, S., Wen, J.T.: A comfort zone set-based approach for coupled temperature and humidity control in buildings. In: IEEE International Conference on Automation Science and Engineering, USA, Aug 2016, pp. 456–461 (2016)Google Scholar
  10. 10.
    Alfano, F.R.A., Olesen, B.W., Palella, B.I., Riccio, G.: Thermal comfort: design and assessment for energy saving. Energy Build. 81, 326–336 (2014)CrossRefGoogle Scholar
  11. 11.
    Hagan, M.T., Demuth, H.B., Beale, M.H., Jesus, O.D.: Neural Network Design, 2nd edn. Martin Hagan, New York (2014)Google Scholar
  12. 12.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1–122 (2011)CrossRefGoogle Scholar
  13. 13.
    Nguyen, D.H., Nguyen, A.T.: A machine learning-based approach for the prediction of electricity consumption. In: 12th Asian Control Conference, Fukuoka (6/2019) (accepted, to be presented)Google Scholar
  14. 14.
    Joshi, M., Morgenstern, A.S., Kremling, A.: Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems. Metab. Eng. 8, 447–455 (2006)CrossRefGoogle Scholar
  15. 15.
  16. 16.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.WPI International Institute for Carbon-Neutral Energy Research (WPI–I2CNER)Kyushu UniversityFukuokaJapan
  2. 2.Institute of Mathematics for Industry (IMI)Kyushu UniversityFukuokaJapan

Personalised recommendations