Gaussian Process Model-Based Short-Term Electric Load Forecasting Using Cuckoo Search

AUTHOR(S):

TITLE

Gaussian Process Model-Based Short-Term Electric Load Forecasting Using Cuckoo Search

ABSTRACT

This paper deals with a Gaussian process model-based short-term electric load forecasting using cuckoo search. The Gaussian process model is a nonparametric model and the output of the model has Gaussian distribution with mean and variance. The multiple Gaussian process models as every hour ahead predictors are used to forecast future electric load demands up to 24 hours ahead in accordance with the direct forecasting approach. The separable least-squares approach that combines the linear least-squares method and cuckoo search is applied to train these Gaussian process models. The results of electric load forecasting for Kyushu district in Japan are shown to demonstrate the effectiveness of the proposed forecasting method.

KEYWORDS

Short-term electric load forecasting, Gaussian process model, Cuckoo search, Separable least-squares method

REFERENCES

[1] T. Ishida and S. Tamaru, Daily electric load forecasting using artificial neural network (in Japanese), IEEJ Trans. B, Vol. 114, No. 11, pp. 1109–1115, 1994.

[2] H. Mori and H. Kobayashi, Optimal fuzzy inference for short-term load forecasting, IEEE Trans. Power Syst., Vol. 11, No. 1, pp. 350–356, 1996.

[3] H. M. A. Hamadi and S. A. Soliman, Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model, Trans. on Electric Power Systems Research, No. 68, pp. 47–59, 2004.

[4] T. Namerikawa and Y. Hosoda, H∞ filter-based short-term electric load prediction considering characteristics of load curve (in Japanese), IEEJ Trans. C, Vol. 132, No. 9, pp. 1446–1453, 2012.

[5] A. O’Hagan, Curve fitting and optimal design for prediction (with discussion), Journal of the Royal Statistical Society B, Vol. 40, pp. 1–42, 1978.

[6] C. K. I. Williams, Prediction with Gaussian processes: from Linear regression to linear prediction and beyond, in Learning and Inference in Graphical Models, Kluwer Academic Press, pp. 599–621, 1998.

[7] C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.

[8] J. Kocijan, A. Girard, B. Banko and R. Murray- Smith, Dynamic systems identification with Gaussian processes, Mathematical and Computer Modelling of Dynamical Systems, Vol. 11, No. 4, pp. 411–424, 2005.

[9] G. Gregorˇciˇc and G. Lightbody, Gaussian process approach for modelling of nonlinear systems, Engineering Applications of Artificial Intelligence, Vol. 22, No. 4-5, pp. 522–533, 2009.

[10] A. Girard, C. E. Rasmussen, J. Q. Candela and R. Murray-Smith, Gaussian process priors with uncertain inputs -application to multiplestep ahead time series forecasting”, in Advances in Neural Information Processing Systems, Vol. 15, pp. 542–552, MIT Press, 2003.

[11] T. Hachino and V. Kadirkamanathan, Multiple Gaussian process models for direct time series forecasting, IEEJ Trans. on Electrical and Electronic Engineering, Vol. 6, No. 3, pp. 245–252, 2011.

[12] T. Hachino, H. Takata, S. Fukushima, Y. Igarashi, Short-term electric load forecasting using multiple Gaussian process models, International Journal of Electrical and Computer Engineering, Vol. 8, No. 2, pp. 447–452, 2014.

[13] X.-S. Yang and S. Deb, Cuckoo search via L´evy flights, Proc. of World Congress on Nature & Biologically Inspired Computing, pp. 210–214, 2009.

[14] S. Ikeda and R. Ooka, Development of metaheuristic optimization methods for operating energy systems including residual battery and thermal storage tank (in Japanese), J. Environ. Eng., Vol. 79, No. 705, pp. 957–966, 2014.

[15] Denki Yohou (Electricity Forecast), Kyushu Electric Power Co., Inc. http://www.kyuden.co.jp/power usages/pc.html

[16] Past Weather Data, Japan Meteorological Agency http://www.data.jma.go.jp/obd/stats/etrn/

Cite this paper

Tomohiro Hachino, Yoshihiro Okuya. (2018) Gaussian Process Model-Based Short-Term Electric Load Forecasting Using Cuckoo Search. International Journal of Power Systems, 3, 27-32