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This content will become publicly available on January 5, 2017

Title: A distributed decision framework for building clusters with different heterogeneity settings

In the past few decades, extensive research has been conducted to develop operation and control strategy for smart buildings with the purpose of reducing energy consumption. Besides studying on single building, it is envisioned that the next generation buildings can freely connect with one another to share energy and exchange information in the context of smart grid. It was demonstrated that a network of connected buildings (aka building clusters) can significantly reduce primary energy consumption, improve environmental sustainability and building s resilience capability. However, an analytic tool to determine which type of buildings should form a cluster and what is the impact of building clusters heterogeneity based on energy profile to the energy performance of building clusters is missing. To bridge these research gaps, we propose a self-organizing map clustering algorithm to divide multiple buildings to different clusters based on their energy profiles, and a homogeneity index to evaluate the heterogeneity of different building clusters configurations. In addition, a bi-level distributed decision model is developed to study the energy sharing in the building clusters. To demonstrate the effectiveness of the proposed clustering algorithm and decision model, we employ a dataset including monthly energy consumption data for 30 buildings where themore » data is collected every 15 min. It is demonstrated that the proposed decision model can achieve at least 13% cost savings for building clusters. Furthermore, the results show that the heterogeneity of energy profile is an important factor to select battery and renewable energy source for building clusters, and the shared battery and renewable energy are preferred for more heterogeneous building clusters.« less
 [1] ;  [2] ;  [3]
  1. Mississippi State Univ., Mississippi State, MS (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  3. Univ. of Illinois at Chicago, Chicago, IL (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 165; Journal ID: ISSN 0306-2619
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
Country of Publication:
United States
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION connected communities; distributed decision making; electricity consumption behavior; genetic algorithm; self-organizing map