TY - GEN
T1 - Choosing the best strategy for energy aware building system
T2 - 28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016
AU - Wang, Yuanyang
AU - Chen, Xiaohong
AU - Sun, Haiying
AU - Chen, Mingsong
PY - 2016
Y1 - 2016
N2 - For many old buildings in the world, due to the legacy devices problem, it is hard to supply appropriate energy for them. In order to reduce the energy consumption of buildings under the premise of satisfying user requirements, we use software control systems whose core part is the scheduling strategy, to reconstruct them. It is time consuming to choose a good scheduling strategy due to many uncertain factors, among which user actions are of the most influence. In this paper, we propose an Support Vector Machine (SVM) based approach to explore the relation between user action and the best scheduling strategy of a control system. The main contributions include: (1) obtaining the sample set by collecting data at the model level using Statistical Model Checking (SMC) based method; (2) using SVM algorithm to learn the relation model between user actions and the best scheduling strategies; and (3) applying the relation model to predict a best scheduling strategy. Finally a real case study is conducted showing the efficiency of our approach.
AB - For many old buildings in the world, due to the legacy devices problem, it is hard to supply appropriate energy for them. In order to reduce the energy consumption of buildings under the premise of satisfying user requirements, we use software control systems whose core part is the scheduling strategy, to reconstruct them. It is time consuming to choose a good scheduling strategy due to many uncertain factors, among which user actions are of the most influence. In this paper, we propose an Support Vector Machine (SVM) based approach to explore the relation between user action and the best scheduling strategy of a control system. The main contributions include: (1) obtaining the sample set by collecting data at the model level using Statistical Model Checking (SMC) based method; (2) using SVM algorithm to learn the relation model between user actions and the best scheduling strategies; and (3) applying the relation model to predict a best scheduling strategy. Finally a real case study is conducted showing the efficiency of our approach.
KW - Energy aware building
KW - Scheduling strategy
KW - Statistical model checking
KW - Support vector machine
KW - User actions
UR - https://www.scopus.com/pages/publications/84988419915
U2 - 10.18293/SEKE2016-237
DO - 10.18293/SEKE2016-237
M3 - 会议稿件
AN - SCOPUS:84988419915
T3 - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
SP - 547
EP - 550
BT - Proceedings - SEKE 2016
PB - Knowledge Systems Institute Graduate School
Y2 - 1 July 2016 through 3 July 2016
ER -
