PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids

Gulsum Gezer, Gurkan Tuna, Dimitris Kogias, Kayhan Gulez, V. Cagri Gungor


Although Smart Grid (SG) transformation brings many advantages to electric utilities, the longstanding challenge for all them is to supply electricity at the lowest cost. In addition, currently, the electric utilities must comply with new expectations for their operations, and address new challenges such as energy efficiency regulations and guidelines, possibility of economic recessions, volatility of fuel prices, new user profiles and demands of regulators. In order to meet all these emerging economic and regulatory realities, the electric utilities operating SGs must be able to determine and meet load, implement new technologies that can effect energy sales and interact with their customers for their purchases of electricity. In this respect, load forecasting which has traditionally been done mostly at city or country level can address such issues vital to the electric utilities. In this paper, an artificial neural network based energy consumption forecasting system is proposed and the efficiency of the proposed system is shown with the results of a set of simulation studies. The proposed system can provide valuable inputs to smart grid applications.


  1. Gungor, V. C., Bin, L., Hancke, G. P. 2010. Opportunities and challenges of wireless sensor networks in smart grid. IEEE Trans. Industrial Electronics, 57(10), 3557-3564.
  2. Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., Hancke, G. P. 2011. Smart Grid Technologies: Communication Technologies and Standards. IEEE Transactions on Industrial Informatics, 7(4), 529-539.
  3. Sood, V. K., Fischer, D., Eklund, J. M., Brown, T. 2009. Developing a communication infrastructure for the smart grid. IEEE Electrical Power and Energy Conference (EPEC 2009), 1-7.
  4. Tuna, G., Gungor, V. C., Gulez, K. 2013. Wireless Sensor Networks for Smart Grid Applications: A Case Study on Link Reliability and Node Lifetime Evaluations in Power Distribution Systems. International Journal of Distributed Sensor Networks, 2013(2013), Article ID 796248, Available from: doi: 10.1155/2013/796248 [Accessed 3rd March 2015].
  5. Alfares, H. K., Nazeeruddin, M. 2002. Electric load forecasting: literature survey and classification of methods. International Journal of Systems Science, 33(1), 3-34.
  6. Gajowniczek, K., Zabkowski, T. 2014. Short term electricity forecasting using individual smart meter data. Procedia Computer Science, 35, 589-597.
  7. Chan, S.C., Tsui, K.M., Wu, H.C., Hou, Y., Wu, Y.-C., Wu, F. F. 2012. Load/Price Forecasting and Management of Demand Response for Smart Grids: Methodologies and Challenges. IEEE Signal Processing Magazine, 29(5), 68-85.
  8. Javed, F., Arshad, N. Wallin, F., Vassileva, I., Dahlquist, E. 2012. Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting. Applied Energy, 96, 150-160.
  9. Liu, N., Tang, Q., Zhang, J., Fan, W., Liu, J. 2014. A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids. Applied Energy, 129, 336-345.
  10. Egri, P., Vancza, J. 2013. Efficient Mechanism for Aggregate Demand Prediction in the Smart Grid. LNAI, 8076, 250-263.
  11. Schachter, J., Mancarella, P. 2014. A Short-term Load Forecasting Model for Demand Response Applications. Proc. 11th International Conference on the European Energy Market (EEM).
  12. Haykin. S. O. 2008. Neural Networks and Learning Machines, Prentice Hall. New York, 3rd edition.
  13. Fazayeli, F., Wang, L., Liu, W. 2008. Back-Propagation with Chaos. IEEE Int. Conference Neural Networks & Signal Processing, 5-8.
  14. Roweis, S. 2009. Levenberg-Marquardt optimization. [Online] Available from: http://www.cs.nyu.edu/ ?roweis/notes/lm.pdf [Accessed 3rd March 2015].
  15. Levenberg, K. 1944. A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 2, 164-168.
  16. Marquardt, D. 1963. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics, 11(2), 431-441.
  17. Ranganathan, A. 2004. The Levenberg-Marquardt Algorithm. Honda Research Institute, USA.
  18. Ziegler J.G., Nichols B. 1942. Optimal setting for automatic controllers. Trans ASME, 64, 759-768.
  19. MathWorks. 2014. Neural Network Toolbox, [Online] Available from: http://www.mathworks.com/products/ neural-network/ [Accessed 2nd March 2015]
  20. Michailidis, E. T., Tuna, G., Gezer, G., Potirakis, S. M., Gulez, K. 2014. ANN-Based Control of a Multiboat Group for the Deployment of an Underwater Sensor Network. International Journal of Distributed Sensor Networks, 2014 (2014), Article ID 786154, Available from: doi: 10.1155/2014/786154 [Accessed 3rd March 2015].

Paper Citation

in Harvard Style

Gezer G., Tuna G., Kogias D., Gulez K. and Gungor V. (2015). PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 110-116. DOI: 10.5220/0005516801100116

in Bibtex Style

author={Gulsum Gezer and Gurkan Tuna and Dimitris Kogias and Kayhan Gulez and V. Cagri Gungor},
title={PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},

in EndNote Style

JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids
SN - 978-989-758-122-9
AU - Gezer G.
AU - Tuna G.
AU - Kogias D.
AU - Gulez K.
AU - Gungor V.
PY - 2015
SP - 110
EP - 116
DO - 10.5220/0005516801100116