Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 5

simulation-based optimization Related Abstracts

5 Prediction-Based Midterm Operation Planning for Energy Management of Exhibition Hall

Authors: Kwang Ryel Ryu, Jeongmin Kim, Doseong Eom

Abstract:

Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.

Keywords: Machine Learning, Operation Planning, Building Energy Management, simulation-based optimization

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4 Short-Term Operation Planning for Energy Management of Exhibition Hall

Authors: Kwang Ryel Ryu, Jeongmin Kim, Yooncheol Lee

Abstract:

This paper deals with the establishment of a short-term operational plan for an air conditioner for efficient energy management of exhibition hall. The short-term operational plan is composed of a time series of operational schedules, which we have searched using genetic algorithms. Establishing operational schedule should be considered the future trends of the variables affecting the exhibition hall environment. To reflect continuously changing factors such as external temperature and occupant, short-term operational plans should be updated in real time. But it takes too much time to evaluate a short-term operational plan using EnergyPlus, a building emulation tool. For that reason, it is difficult to update the operational plan in real time. To evaluate the short-term operational plan, we designed prediction models based on machine learning with fast evaluation speed. This model, which was created by learning the past operational data, is accurate and fast. The collection of operational data and the verification of operational plans were made using EnergyPlus. Experimental results show that the proposed method can save energy compared to the reactive control method.

Keywords: Energy Management, predictive model, simulation-based optimization, exhibition hall

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3 Simulation-Based Optimization of a Non-Uniform Piezoelectric Energy Harvester with Stack Boundary

Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

Abstract:

This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: Energy harvesting, Genetic Algorithm, Piezoelectricity, Artificial Neural Network, simulation-based optimization

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2 Improvement of Central Composite Design in Modeling and Optimization of Simulation Experiments

Authors: A. Nuchitprasittichai, N. Lerdritsirikoon, T. Khamsing

Abstract:

Simulation modeling can be used to solve real world problems. It provides an understanding of a complex system. To develop a simplified model of process simulation, a suitable experimental design is required to be able to capture surface characteristics. This paper presents the experimental design and algorithm used to model the process simulation for optimization problem. The CO2 liquefaction based on external refrigeration with two refrigeration circuits was used as a simulation case study. Latin Hypercube Sampling (LHS) was purposed to combine with existing Central Composite Design (CCD) samples to improve the performance of CCD in generating the second order model of the system. The second order model was then used as the objective function of the optimization problem. The results showed that adding LHS samples to CCD samples can help capture surface curvature characteristics. Suitable number of LHS sample points should be considered in order to get an accurate nonlinear model with minimum number of simulation experiments.

Keywords: central composite design, simulation-based optimization, CO2 liquefaction, latin hypercube sampling

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1 A Continuous Review Policy for Two Complementary Products with Interrelated Demand

Authors: Saeed Poormoaied

Abstract:

Inspired by the joint demand nature of complementary products, we consider a single echelon supply chain consisting of a single retailer with two complementary products. Due to interrelated demands exist between complementary products, developing an insightful control policy is challenging. The two products are complementary in the sense that they are always purchased and demanded jointly or in sets of one unit of each. The retailer relies on a continuous review (Q; r) type control policy for both products in an infinite planning horizon setting. The customers arrive according to a Poisson process, and unmet demands are treated as lost sales. In case of stock out of one product, the complementary product's demand is lost as well. This study aimed to identify the optimal parameters and cost-effective performance of the (Q, r) policy for complementary products with interrelated demands in order to achieve the maximum profit in the system. Due to the uncertainty of the demand process, tracking the stock-out periods affecting the demand rates of complementary products is difficult. To cope with this difficulty, we utilize a simulation-based optimization approach to elicit the optimal parameters of the (Q; r) policy for each product. Experimental analyses enable us to figure out the effect of the joint demand property of complementary products on the continuous review control policy behavior and the total profit of the inventory system. Our numerical results show that: (i) the maximum profit in inventory systems with complementary products is achieved when we have full joint demands and no demand with one unit of each; (ii) when demand rate is low, the interactions between demands have significant impact on the expected total profit, and this effect becomes more considerable when the unit lost sale costs are large. Deriving analytical results for a single-product inventory system, we develop an approximation algorithm to find a near-to-optimal solution. Numerical experiences reveal that the solution obtained by the approximation algorithm is very close to the optimal solution obtained by simulation.

Keywords: simulation-based optimization, approximation algorithm, complementary products, continuous review policy

Procedia PDF Downloads 11