A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty

There are a large number of real-world engineering design problems that are multi-objective and multiconstrained, having uncertainty in their inputs. Robust optimization is developed to obtain solutions that are optimal and less sensitive to uncertainty. Since most of complex engineering design problems rely on time-consuming simulations, the robust optimization approaches may become computationally intractable. To address this issue, an advanced multi-objective robust optimization approach based on Kriging model and support vector machine (MORO-KS) is proposed in this work. First, the main problem in MORO-KS is iteratively restricted by constraint cuts formed in the subproblem. Second, each objective function is approximated by a Kriging model to predict the response value. Third, a support vector machine (SVM) classifier is constructed to replace all constraint functions classifying design alternatives into two categories: feasible and infeasible. The proposed MORO-KS approach is tested on two numerical examples and the design optimization of a micro-aerial vehicle (MAV) fuselage. Compared with the results obtained from other MORO approaches, the effectiveness and efficiency of the proposed MORO-KS approach are illustrated.

Download Full-text

Energy Management in Smart Building by a Multi-Objective Optimization Model and Pascoletti-Serafini Scalarization Approach

Processes ◽

10.3390/pr9020257 ◽

2021 ◽

Vol 9 (2) ◽

pp. 257

Author(s):

Zahra Foroozandeh ◽

Sérgio Ramos ◽

João Soares ◽

Zita Vale

Keyword(s):

Linear Programming ◽

Energy Management ◽

Residential Buildings ◽

Peak Load ◽

Electricity Consumption ◽

Mixed Integer ◽

Total Energy Consumption ◽

Smart Building ◽

Multi Objective ◽

Consumption Cost

Generally, energy management in smart buildings is formulated by mixed-integer linear programming, with different optimization goals. The most targeted goals are the minimization of the electricity consumption cost, the electricity consumption value from external power grid, and peak load smoothing. All of these objectives are desirable in a smart building, however, in most of the related works, just one of these mentioned goals is considered and investigated. In this work, authors aim to consider two goals via a multi-objective framework. In this regard, a multi-objective mixed-binary linear programming is presented to minimize the total energy consumption cost and peak load in collective residential buildings, considering the scheduling of the charging/discharging process for electric vehicles and battery energy storage system. Then, the Pascoletti-Serafini scalarization approach is used to obtain the Pareto front solutions of the presented multi-objective model. In the final, the performance of the proposed model is analyzed and reported by simulating the model under two different scenarios. The results show that the total consumption cost of the residential building has been reduced 35.56% and the peak load has a 45.52% reduction.

Download Full-text

A Multi-Objective Robust Optimization Approach Under Interval Uncertainty Based on Kriging and Support Vector Machine

Volume 2B: 44th Design Automation Conference ◽

10.1115/detc2018-86237 ◽

2018 ◽

Author(s):

Tingli Xie ◽

Ping Jiang ◽

Qi Zhou ◽

Leshi Shu ◽

Yang Yang

Keyword(s):

Support Vector Machine ◽

Robust Optimization ◽

Kriging Model ◽

Interval Uncertainty ◽

Support Vector ◽

Optimization Approach ◽

Multi Objective ◽

Large Numbers ◽

Design Alternatives ◽

Engineering Design Problems

Interval uncertainty can cause uncontrollable variations in the objective and constraint values, which could seriously deteriorate the performance or even change the feasibility of the optimal solutions. Robust optimization is to obtain solutions that are optimal and minimally sensitive to uncertainty. Because large numbers of complex engineering design problems depend on time-consuming simulations, the robust optimization approaches might become computationally intractable. To address this issue, a multi-objective robust optimization approach based on Kriging and support vector machine (MORO-KS) is proposed in this paper. Firstly, the feasible domain of main problem in MORO-KS is iteratively restricted by constraint cuts formed in the subproblem. Secondly, each objective function is approximated by a Kriging model to predict the response value. Thirdly, a Support Vector Machine (SVM) model is constructed to replace all constraint functions classifying design alternatives into two categories: feasible and infeasible. A numerical example and the design optimization of a microaerial vehicle fuselage are adopted to test the proposed MORO-KS approach. Compared with the results obtained from the MORO approach based on Constraint Cuts (MORO-CC), the effectiveness and efficiency of the proposed MORO-KS approach are illustrated.

Download Full-text

A New Efficient and Useful Robust Optimization Approach - Design for Multi-Objective Six Sigma

2005 IEEE Congress on Evolutionary Computation ◽

10.1109/cec.2005.1554785 ◽

2005 ◽

Cited By ~ 11

Author(s):

K. Shimoyama ◽

A. Oyama ◽

K. Fujii

Keyword(s):

Robust Optimization ◽

Six Sigma ◽

Optimization Approach ◽

Multi Objective

Download Full-text

A sequential multi-objective robust optimization approach under interval uncertainty based on support vector machines

2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) ◽

10.1109/ieem.2017.8290260 ◽

2017 ◽

Author(s):

Tingli Xie ◽

Qi Zhou ◽

Jiexiang Hu ◽

Leshi Shu ◽

Ping Jiang

Keyword(s):

Support Vector Machines ◽

Robust Optimization ◽

Interval Uncertainty ◽

Support Vector ◽

Optimization Approach ◽

Multi Objective ◽

Vector Machines

Download Full-text

Sequential Cooperative Robust Optimization (SCRO) for Multi-Objective Design Under Uncertainty

Volume 2B: 41st Design Automation Conference ◽

10.1115/detc2015-47885 ◽

2015 ◽

Cited By ~ 1

Author(s):

J. M. Hamel

Keyword(s):

Robust Optimization ◽

Uncertainty Analysis ◽

Computational Efficiency ◽

System Optimization ◽

Surrogate Models ◽

Optimization Techniques ◽

Robust Design Optimization ◽

Optimization Approach ◽

Deterministic System ◽

Multi Objective

The optimal design of systems under uncertainty is a critical challenge faced by design engineers. Robust optimization is a well-studied and widely used technique for the design of engineering systems that possess uncertainty, and numerous robust optimization techniques have been presented in recent years. The majority of the robust optimization techniques presented in the literature suffer from a computational efficiency challenge, either due to the expense of obtaining objective or constraint function uncertainty information, or due to the fact that many robust optimization approaches (with a few notable exceptions) require that a potentially expensive uncertainty analysis calculation (e.g. Monte-Carlo simulation) be nested within an already potentially expensive optimization solver (e.g. a genetic algorithm). Additionally, many robust optimization approaches focus solely on design problems that possess a single design objective, and the robust techniques that do consider problems with multiple design objectives often require various simplifying assumptions or are even more computationally expensive to implement. Clearly there are opportunities for improvement in the area of robust optimization, and this paper presents a new robust design Optimization approach called Sequential Cooperative Robust Optimization (SCRO), which uses both a sequential approach and multi-objective optimization techniques in an effort to decouple the deterministic system optimization problem from the associated uncertainty analysis problem. The SCRO approach first fits surrogate models to the system objective and constraint functions, in addition to system sensitivity functions, using as few function calls as possible in order to improve computational efficiency. The approach then performs a series of sequential multi-objective optimizations using the developed surrogate models. These optimizations work to find points in the design space that are optimal with respect to deterministic performance and both objective and feasibility robustness metrics based on predicted system sensitivities. The SCRO approach has the potential to find solutions not available to other robust optimization approaches, and can be more efficient than other more traditional robust optimization techniques due to its use of surrogate approximation and a sequential framework.

Download Full-text