DynaGrow – Multi-Objective Optimization for Energy Cost-efficient Control of Supplemental Light in Greenhouses

In this research, we propose a multi-objective optimization framework to minimize the energy cost while maintain the indoor air quality. The proposed framework is consisted with two stages: predictive modeling stage and multi-objective optimization stage. In the first stage, artificial neural networks are applied to predict the energy utility in real-time. In the second stage, an optimization algorithm namely firefly algorithm is utilized to reduce the energy cost while maintaining the required IAQ conditions. Industrial data collected from a commercial building in central business district in Chengdu, China is utilized in this study. The results produced by the optimization framework show that this strategy reduces energy cost by optimizing operations within the HAVC system.

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Location-aware multi-objective optimization for energy cost management in semi-public buildings using thermal discomfort information

Sustainable Cities and Society ◽

10.1016/j.scs.2017.12.021 ◽

2018 ◽

Vol 40 ◽

pp. 174-181 ◽

Cited By ~ 6

Author(s):

Pradnya Gaonkar ◽

Jyotsna Bapat ◽

Debabrata Das

Keyword(s):

Energy Cost ◽

Cost Management ◽

Multi Objective Optimization ◽

Public Buildings ◽

Thermal Discomfort ◽

Multi Objective ◽

Location Aware

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Improving Energy Efficiency in Industry 4.0 With Multi-Objective Optimization

Advances in Business Information Systems and Analytics - Handbook of Research on Integrating Industry 4.0 in Business and Manufacturing ◽

10.4018/978-1-7998-2725-2.ch010 ◽

2020 ◽

pp. 213-226

Author(s):

Lejla Banjanović-Mehmedović ◽

Fahrudin Mehmedović

Keyword(s):

Energy Efficiency ◽

Working Conditions ◽

Energy Cost ◽

Industry 4.0 ◽

Optimization Problems ◽

Multi Objective Optimization ◽

Efficiency Optimization ◽

Assigned Value ◽

Multi Objective ◽

Important Challenge

Improvements in energy efficiency in Industry 4.0 is the main imperative in manufacturing. An important challenge in many fields of complex industrial processes is energy-efficient optimization. The basic idea of the energy efficiency optimization algorithm is to find the optimal assigned value of process parameters to achieve the lowest energy cost and the best working conditions. Applying the multi-objective approach for solving real industry optimization problems is a challenging task. Therefore, this chapter provides an overview of the most significant issues in multi-objective optimization problem. Improving energy efficiency with the multi-objective optimization has opened new opportunities for technological progress in Industry 4.0.

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Operation Optimization of Regional Integrated Energy System Based on Electric Heating Gas Demand Response

E3S Web of Conferences ◽

10.1051/e3sconf/202021801002 ◽

2020 ◽

Vol 218 ◽

pp. 01002

Author(s):

Nan Wang ◽

Jialin Yang ◽

Xichao Zhou ◽

Zhen Li ◽

Yaling Sun ◽

...

Keyword(s):

Power Consumption ◽

Wind Power ◽

Demand Response ◽

Energy Cost ◽

Energy System ◽

Multi Objective Optimization ◽

Total Energy Consumption ◽

Multi Objective ◽

Trade Offs ◽

Integrated Energy System

Taking into account the energy cost, pollution emission, wind power consumption, and other dispatching objectives in the regionally integrated energy system (RIES), the RIES multi-objective optimization model considering the integrated demand response is established. Firstly, the RIES modeling of equipment including electricity-to-gas, energy storage systems, cogeneration units, etc., and the introduction of a comprehensive demand response that specifically considers load reduction, load transfer, and load replacement in the region, aimed at reducing system load peaks and valleys difference. Then, the objective function to minimize the system energy cost, the abandoned wind power, and the pollutant treatment cost was established respectively, and the multi-objective optimization method was adopted—the Pareto front was solved by fuzzy weighted programming traversal weights, and then the decision was made based on evidence Method to find the optimal scheduling strategy. Finally, based on a typical case study, the results show that the proposed multi-objective optimization algorithm can effectively make trade-offs among multiple scheduling objectives, and RIES considering comprehensive demand response has advantages in terms of total energy consumption, environmental friendliness, and wind power consumption.

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