Bayesian Networks for Supporting Model Based Predictive Control of Smart Buildings ( Dynamic Programming and Bayesian Inference, Concepts and Applications )

Publication series ： Dynamic Programming and Bayesian Inference, Concepts and Applications

Author： Alessandro Carbonari Massimo Vaccarini and Alberto Giretti  

Publisher： IntechOpen‎

Publication year： 2014

E-ISBN: INT5700558470

P-ISBN(Paperback): 9789535113645

P-ISBN(Hardback):  9789535113645

Subject： O211 probability (probability theory, probability theory)

Keyword： 概率论（几率论、或然率论）

Language： ENG

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Bayesian Networks for Supporting Model Based Predictive Control of Smart Buildings

Chapter

Chapter 1 Bayesian Networks for Supporting Model Based Predictive Control of Smart Buildings

1. Introduction

2. Literature review about MPC control

3. Basics of Bayesian Networks

3.1. Inference propagation

3.2. Learning conditional probability tables

4. Practical implementation of the Bayesian predictor

4.1. The metrics used for validation

4.2. Instantiation of BNs for MPC

5. Development of the Bayesian models and cost function

5.1. The procedure

5.2. The cost function

5.3. The networks and their estimates

6. The Passeig de Gracia simulator case study

6.1. The SEAM4US simulator

6.2. How the MPC controller works

7. Conclusions

8. List of notations

Author details

References

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