LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning
Open Access
- 17 January 2021
- journal article
- research article
- Published by MDPI AG in Sustainability
- Vol. 13 (2), 894
- https://doi.org/10.3390/su13020894
Abstract
The growing interest in saving building energy has increasingly motivated studies on model predictive control (MPC), where system operation proceeds according to a planned operation strategy. Data-driven models that perform learning using past operation data of buildings are favorable for MPC applications owing to their fast computation speed. However, it is difficult to apply MPC to buildings with insufficient operation data, as the prediction accuracy varies depending on the data used for learning. To address this, we propose a method that involves generating data through a detailed building energy model and utilizing a long short-term memory (LSTM) network that performs learning using the data as an MPC model. The model was verified through a comparison with the reference model using the same optimization algorithm. In the MPC of the objective function, which is to reduce electrical energy expenditure by optimizing the indoor temperature of the target building, approximately 35% grid energy consumption was reduced compared to a reference case, by increasing self-consumption of photovoltaic (PV) energy and avoiding PV curtailment. Further, the required computation time was reduced to approximately 30%, even including the data generation time for daily learning, thereby confirming the feasibility of the MPC model that employs LSTM.Funding Information
- Korea Institute of Energy Technology Evaluation and Planning (2019271010015D)
This publication has 37 references indexed in Scilit:
- Building modeling: Selection of the most appropriate model for predictive controlEnergy and Buildings, 2012
- Two-Dimensional Boron Monolayer SheetsACS Nano, 2012
- Study of the optimal control problem formulation for modulating air-to-water heat pumps connected to a residential floor heating systemEnergy and Buildings, 2012
- Optimization of an HVAC system with a strength multi-objective particle-swarm algorithmEnergy, 2011
- Model predictive control of a building heating system: The first experienceEnergy and Buildings, 2011
- Neuro-optimal operation of a variable air volume HVAC&R systemApplied Thermal Engineering, 2010
- Advanced control systems engineering for energy and comfort management in a building environment—A reviewRenewable and Sustainable Energy Reviews, 2009
- HVAC system optimization—in-building sectionEnergy and Buildings, 2005
- Evaluation of optimal control for active and passive building thermal storageInternational Journal of Thermal Sciences, 2004
- Long Short-Term MemoryNeural Computation, 1997