8,958 research outputs found
Buildings-to-Grid Integration Framework
This paper puts forth a mathematical framework for Buildings-to-Grid (BtG)
integration in smart cities. The framework explicitly couples power grid and
building's control actions and operational decisions, and can be utilized by
buildings and power grids operators to simultaneously optimize their
performance. Simplified dynamics of building clusters and building-integrated
power networks with algebraic equations are presented---both operating at
different time-scales. A model predictive control (MPC)-based algorithm that
formulates the BtG integration and accounts for the time-scale discrepancy is
developed. The formulation captures dynamic and algebraic power flow
constraints of power networks and is shown to be numerically advantageous. The
paper analytically establishes that the BtG integration yields a reduced total
system cost in comparison with decoupled designs where grid and building
operators determine their controls separately. The developed framework is
tested on standard power networks that include thousands of buildings modeled
using industrial data. Case studies demonstrate building energy savings and
significant frequency regulation, while these findings carry over in network
simulations with nonlinear power flows and mismatch in building model
parameters. Finally, simulations indicate that the performance does not
significantly worsen when there is uncertainty in the forecasted weather and
base load conditions.Comment: In Press, IEEE Transactions on Smart Gri
Energy Disaggregation for Real-Time Building Flexibility Detection
Energy is a limited resource which has to be managed wisely, taking into
account both supply-demand matching and capacity constraints in the
distribution grid. One aspect of the smart energy management at the building
level is given by the problem of real-time detection of flexible demand
available. In this paper we propose the use of energy disaggregation techniques
to perform this task. Firstly, we investigate the use of existing
classification methods to perform energy disaggregation. A comparison is
performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors,
Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted
Boltzmann Machine to automatically perform feature extraction. The extracted
features are then used as inputs to the four classifiers and consequently shown
to improve their accuracy. The efficiency of our approach is demonstrated on a
real database consisting of detailed appliance-level measurements with high
temporal resolution, which has been used for energy disaggregation in previous
studies, namely the REDD. The results show robustness and good generalization
capabilities to newly presented buildings with at least 96% accuracy.Comment: To appear in IEEE PES General Meeting, 2016, Boston, US
- …