AN OPEN- SOURCE SOFTWARE PLATFORM FOR MODEL PREDICTIVE CONTROL IN BUILDINGS

Transcription

1 Haystack Software Tools and Applications Part 2 AN OPEN- SOURCE SOFTWARE PLATFORM FOR MODEL PREDICTIVE CONTROL IN BUILDINGS LISA RIVALIN, PH.D. ENGIE AXIMA/LBNL DAVID BLUM, PH.D. LBNL May 10, 2017

2 AN OPEN- SOURCE SOFTWARE PLATFORM FOR MODEL PREDICTIVE CONTROL IN BUILDINGS ENGIE Axima presentation Context and background Challenges Our Work Conclusion May 10,

3 ENGIE Global Energy player Strategy - Decarbonization - Digitalization - Decentralization Energy Efficiency 154,950 employees in 70 countries May 10,

4 ENGIE AXIMA French Leader in: - Climate Engineering - Refrigeration - Fire protection - Process environment control 7,500 employees in 140 branches 1,3 billions turnover May 10,

5 FUTURE REQUIREMENTS OF BUILDING SYSTEMS Energy and carbon reduction Electric grid integration District energy interactions Occupant- responsive Predictive maintenance IoT Ready May 10,

6 CONVENTIONAL CONTROL May 10,

7 MODEL PREDICTIVE CONTROL (MPC) May 10,

8 MODEL PREDICTIVE CONTROL (MPC) Consider future disturbances and incentives Coordinate multiple systems Occupant integration Facility operator Feedback May 9,

9 CHALLENGES Building installation Data Management Integration MPC specific challenges May 9,

10 BUILDING INSTALLATION Sensing and control limitations - Location of sensors - Frequency and resolution - Unmeasured states - Indirect control of states Model and optimization setup - MPC setup and maintenance cost - Adapt to building change May 9,

11 DATA MANAGEMENT Gathering and storing Access and security How equipment connect with each other? Configuration parameters of control systems May 9,

12 INTEGRATION Metadata tagging : need of a standard semantic - Location of sensors and equipment - Connections between data Metadata is not machine readable Metadata is not consistent between vendors May 9,

13 MPC SPECIFIC CHALLENGES Performance Models Optimization problem classification - White Box Accurate if correct High cost uncertainties - Grey Box Accurate enough Hard to structure and estimate parameters - Black Box Accurate in trained range Must have enough data Real building problematic Compressor efficiency, fan power On/off multi- stage, variable speed Imperfect weather, internal load forecasts Peak demand minimization White, grey or back box modeling Optimization problem classification Nonlinear, nonconvex programming Binary of mixed integer programming Stochastic optimization Min- max problem Gradient- based or numeric algorithms May 9,

14 LBNL WORK : MPCPY Features: - Exogenous data collection - Building system emulation or data collection - Control optimization - Adaptive model learning - Non- MPC- expert usability Design - Python scripting - Utilize support software - Extensible - Open- source Development work has been funded by CERC - U.S.- China Clean Energy Research Center ( china- cerc.org/). D. H. Blum and M. Wetter MPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings. Proceedings of the 15th Conference of International Building Performance Simulation, Aug 7 9, San Francisco, CA, Accepted. May 9,

15 ENGIE AXIMA S WORK BIM Life: - 3D model of the building - Active database - Better coordination within teams Smart Buildings - Predictive Maintenance - Automated buildings - Energy Consumption Reduction - Design Assistance - User decision assistance May 9,

16 CONCLUSIONS MPC can provide solutions to many problems buildings have to solve now and in the future However, it is difficult to realize in practice Haystack Project can overcome some challenges LBNL is working to provide a software framework, MPCPy, that is demonstrated and distributed freely. ENGIE Axima is working to practically use MPC for the future Smart Buildings May 9,

17 PERSPECTIVE Apply Haystack on several buildings MPCPY expansion, testing and maintenance Model library development and testing Demonstration tests (LBNL partners and ENGIE Axima) Facilitate community consensus on best- practice MPC methods May 9,