THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

online first only

Procedure for creating custom MLR-based STLF models by using GA optimization

ABSTRACT
This paper presents a novel procedure for short-term load forecasting (STLF) in distribution management systems (DMS). The load is forecasted for feeders that can be of a primarily residential, commercial, industrial or combined type. Each feeder has various amounts of distributed energy resources (DER) installed, which accounts for multiple different load patterns. Hence, the DMS cannot use a single STLF model for all forecasts. The proposed procedure addresses the specificity of each particular feeder type, by creating customized STLF models. It uses a genetic algorithm (GA) to select the best inputs for different multiple linear regression (MLR) models. The GA chooses variables from a dataset constructed using load and temperature measurements. The dataset is extended by adding nonlinear transformations and mutual interaction effects of the measurements, as well as calendar variables. This extension enables for the modelling of nonlinear influences and extracts the nonlinearity to the domain of input variables. The models' performance is assessed by the mean absolute percentage error (MAPE). The proposed procedure is applied to a set of measurements collected from a US electric power utility, which operates in the city of Burbank, CA. The obtained MLR model is compared with a previously proposed naïve benchmark, and a special comparison model, developed by correlation analysis. The proposed method is extendable to suit DMS systems with different types of electricity consumers.
KEYWORDS
PAPER SUBMITTED: 2019-12-05
PAPER REVISED: 2020-01-25
PAPER ACCEPTED: 2020-02-07
PUBLISHED ONLINE: 2020-03-08
DOI REFERENCE: https://doi.org/10.2298/TSCI191205101I
REFERENCES
  1. T. Peng, N. Hubele and G. Karady, "An adaptive neural network approach to one-week ahead load forecasting," Power Systems, IEEE Transactions on, vol. 8, no. 3, pp. 1195-1203, Aug 1993.
  2. S. Pappas, L. Ekonomou, P. Karampelas, D. Karamousantas, S. Katsikas, G. Chatzarakis and P. Skafidas, "Electricity demand load forecasting of the Hellenic power system using an ARMA model," Electric Power Systems Research, vol. 80, no. 3, pp. 256-264, March 2010.
  3. A. Bracale, G. Carpinelli, P. De Falco and T. Hong, Short-term industrial reactive power forecasting, vol. 107, International Journal of Electrical Power & Energy Systems, 2019, pp. 177-185.
  4. H. Hippert, C. Pedreira and R. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans Power Syst, vol. 16, no. 1, pp. 44-45, 2001.
  5. M. López, S. Valero, C. Senabre, J. Aparicio and A. Gabaldon, "Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study," Electric Power Systems Research, vol. 91, pp. 18-27, October 2012.
  6. S. Ilić, A. Erdeljan, F. Kulić and S. Vukmirović, "Hybrid artificial neural network system for short-term load forecasting," Thermal Science, vol. 16, no. 1, pp. 215-224, 2012.
  7. R.-A. Hooshmand, H. Amooshahi and M. Parastegari, "A hybrid intelligent algorithm based short-term load forecasting approach," International Journal of Electrical Power & Energy Systems, vol. 45, no. 1, pp. 313-324, February 2013.
  8. M. Amina, V. Kodogiannis, I. Petrounias and D. Tomtsis, "A hybrid intelligent approach for the prediction of electricity consumption," International Journal of Electrical Power & Energy Systems, vol. 43, no. 1, pp. 99-108, December 2012.
  9. P. Singh, P. Dwivedi and V. Kant, A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting, vol. 174, 2019, pp. 460-477.
  10. N. Amjady and A. Daraeepour, "Mixed price and load forecasting of electricity markets by a new iterative prediction method," Electric Power Systems Research, vol. 79, no. 9, pp. 1329-1336, September 2009.
  11. N. Amjady and F. Keynia, "Electricity market price spike analysis by a hybrid data model and feature selection technique," Electric Power Systems Research, vol. 80, no. 3, pp. 318-327, March 2010.
  12. N. Bigdeli, K. Afshar and N. Amjady, "Market data analysis and short-term price forecasting in the Iran electricity market with pay-as-bid payment mechanism," Electric Power Systems Research, vol. 79, no. 6, pp. 888-898, June 2009.
  13. N. Amjady, F. Keynia and H. Zareipour, "Short-term wind power forecasting using ridgelet neural network," Electric Power Systems Research, vol. 81, no. 12, pp. 2099-2107, December 2011.
  14. M. Matijaš, J. A. Suykens and S. Krajcar, "Load forecasting using a multivariate meta-learning system," Expert Systems with Applications, vol. 40, no. 1, p. 4427-4437, September 2013.
  15. J. J. Cárdenas, L. Romeral, A. Garcia and F. Andrade, "Load forecasting framework of electricity consumptions for an Intelligent Energy Management System in the user-side," Expert Systems with Applications, vol. 39, no. 5, p. 5557-5565, April 2012.
  16. T. Hong, P. Wang and H. L. Willis, "A Naïve Multiple Linear Regression Benchmark for Short Term Load Forecasting," in Power and Energy Society General Meeting, 2011 IEEE, Raleigh, NC, USA, 24-29 July 2011.
  17. I. Drezga and S. Rahman, "Input variable selection for ANN-based short-term load forecasting," Power Systems, IEEE Transactions on, vol. 13, no. 4, pp. 1238-1244, Nov 1998.
  18. X. Zhi, Shi-Jie Ye, Z. Bo and S. Cai-Xin, "BP neural network with rough set for short term load forecasting," Expert Systems with Applications, vol. 36, no. 1, p. 273-279, January 2009.
  19. Y. Hu, J. Li, M. Hong, J. Ren, R. Lin, Y. Liu, M. Liu and Y. Man, Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process, vol. 170, Energy, 2019, pp. 1215-1227.
  20. Z. Miljanić, I. Djurović and I. Vujošević, "Optimal placement of PMUs with limited number of channels," Electric Power Systems Research, vol. 90, pp. 93-98, September 2012.