428 research outputs found
Integration and optimal control of microcsp with building hvac systems: Review and future directions
Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost
Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks
Modern buildings encompass complex dynamics of multiple electrical,
mechanical, and control systems. One of the biggest hurdles in applying
conventional model-based optimization and control methods to building energy
management is the huge cost and effort of capturing diverse and temporally
correlated dynamics. Here we propose an alternative approach which is
model-free and data-driven. By utilizing high volume of data coming from
advanced sensors, we train a deep Recurrent Neural Networks (RNN) which could
accurately represent the operation's temporal dynamics of building complexes.
The trained network is then directly fitted into a constrained optimization
problem with finite horizons. By reformulating the constrained optimization as
an unconstrained optimization problem, we use iterative gradient descents
method with momentum to find optimal control inputs. Simulation results
demonstrate proposed method's improved performances over model-based approach
on both building system modeling and control
Economic MPC of Nonlinear Systems with Non-Monotonic Lyapunov Functions and Its Application to HVAC Control
This paper proposes a Lyapunov-based economic MPC scheme for nonlinear sytems
with non-monotonic Lyapunov functions. Relaxed Lyapunov-based constraints are
used in the MPC formulation to improve the economic performance. These
constraints will enforce a Lyapunov decrease after every few steps. Recursive
feasibility and asymptotical convergence to the steady state can be achieved
using Lyapunov-like stability analysis. The proposed economic MPC can be
applied to minimize energy consumption in HVAC control of commercial buildings.
The Lyapunov-based constraints in the online MPC problem enable the tracking of
the desired set-point temperature. The performance is demonstrated by a virtual
building composed of two adjacent zones
- …