Name
Abstract | ||
|---|---|---|
The building thermal capacity can be used for shifting on-peak load and reducing peak cooling/heating in commercial and residential buildings. The optimization of the pre-cooling/pre-heating is a complicated problem of several major factors, including utility rates, load profiles, building storage characteristics and weather conditions. This paper introduces an intelligent search algorithm, Particle Swarm Optimization (PSO), to find the near optimal solution. A simulation model of a single floor with multi-zone based on a real lab building in Carnegie Mellon University is built with Energyplus to simulate the proposed algorithm. By using MLE+, the EnergyPlus model of the building becomes an S-function block in the Matlab/Simulink, and all available Matlab toolboxes can be used for control and optimization purposes. Simulation results demonstrate the feasibility and the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CoASE.2013.6654018 | CASE |
Keywords | Field | DocType |
load shifting,near optimal solution,mle+,energyplus,pso,particle swarm optimisation,intelligent search algorithm,search problems,electrical engineering computing,intelligent optimization,optimal building energy management,carnegie mellon university,real lab building,s-function block,building management systems,particle swarm optimization | Particle swarm optimization,Mathematical optimization,MATLAB,Building management system,Search algorithm,Multi-swarm optimization,Building energy,Engineering,Metaheuristic | Conference |
ISSN | Citations | PageRank |
2161-8070 | 0 | 0.34 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinliang Xu | 1 | 162 | 21.35 |
| Kun Ji | 2 | 0 | 1.01 |
| Yan Lu | 3 | 0 | 0.34 |
| Yuebin Yu | 4 | 0 | 0.34 |
| Wenxin Liu | 5 | 63 | 11.65 |