A Tool for Learning Dynamic Bayesian Networks for Forecasting
- 1 Citations
- 1.2k Downloads
Abstract
Renewable energy is increasing its participation in power generation in many countries. In Mexico, the strategy is to generate 35 % of electricity from renewable sources by 2024. Currently only 18.3 % of the generated energy is obtained from renewable and clean sources. The integration of renewable energies in the energy market is a challenge due to their high variability, instability and uncertainty. Hence, energy forecast is the required service by the power generators to offer energy with certain degree of confidence. Dynamic Bayesian networks (DBNs) have proved to be an appropriate mechanism for uncertainty and time reasoning; however there is no basic tool that builds DBN using time series for a process. This paper describes the design, construction and tests for a DBNs learning tool. This tool has already been used to construct dynamic models for wind power forecast and in this paper it is used to describe the variation of the dam level caused by rainfall in a hydroelectric power plant.
References
- 1.Bayes net toolbox for matlab. Technical report, University of British Columbia, Canada (1997–2002)Google Scholar
- 2.Genie graphical network interface to smile software package to create decision theoretic models. Technical report, Pittsburgh University, USA (1998)Google Scholar
- 3.Andersen, S.K., Olesen, K.G., Jensen, F.V., Jensen, F.: Hugin: a shell for building Bayesian belief universes for expert systems. In: Proceedings of the Eleventh Joint Conference on Artificial Intelligence, IJCAI, 20–25 August 1989, Detroit, Michigan, USA, pp. 1080–1085 (1989)Google Scholar
- 4.Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–348 (1992)zbMATHGoogle Scholar
- 5.The Elvira Consortium: Elvira: An environment for creating and using probabilistic graphical models. In: Proceedings of the First European Workshop on Probabilistic graphical models (PGM 2002), pp. 1–11, Cuenca, Spain (2002)Google Scholar
- 6.Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar
- 7.Ibargüengoytia, P.H., Reyes, A.: On-line diagnosis of a power generation process using probabilistic models. In: 16th International Conference on Intelligent Systems Application to Power Systems, ISAP 2011, Hersonissos, Crete Greece. IEEE PES (2011)Google Scholar
- 8.Ibargüengoytia, P.H., Reyes, A., Romero, I., Pech, D., García, U.: Evaluating probabilistic graphical models for forecasting. In: International Conference on Intelligent Systems Application to Power Systems, ISAP 2015, Porto, Portugal. IEEE PES (2015)Google Scholar
- 9.Ibargüengoytia, P.H., Reyes, A., Romero-Leon, I., Pech, D., García, U.A., Sucar, L.E., Morales, E.F.: Wind power forecasting using dynamic Bayesian models. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds.) MICAI 2014, Part II. LNCS, vol. 8857, pp. 184–197. Springer, Heidelberg (2014) Google Scholar
- 10.Murphy, K.P.: Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, Berkeley, CA, USA (2002)Google Scholar
- 11.Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988) Google Scholar
- 12.Spirtes, P., Glymour, C., Sheines, R.: Causation, Prediction and Search. MIT Press, Cambridge (2000) Google Scholar