Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands

Ahn, Jonghoon; Cho, Soolyeon; Chung, Dae Hun

doi:10.1016/j.apenergy.2016.12.155

Cited by 60 publications

(32 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…subject to constraints (1)-(2), (5)-(7), (9)- (12), and (14)- (21). The first line represents the day-ahead bidding cost of the central controller of CBs, the second line shows the real-time electricity cost of the central controller of CBs, the third line is the penalty cost for differences between the day-ahead and real-time electricity buy from or sell to a retail electricity market, the fourth line expresses the degradation costs of PEVs, the fifth line denotes the degradation costs of ES units, and the last line is the generation costs of gas-based heat boilers.…”

Section: Objective Function Related With Operating Costsmentioning

confidence: 99%

Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market

et al. 2019

View full text Add to dashboard Cite

Demand response has been implemented by distribution system operators to reduce peak demand and mitigate contingency issues on distribution lines and substations. Specifically, the campus-based commercial buildings make the major contributions to peak demand in a distribution system. Note that prior works neglect the consumers' comfort level in performing demand response, which limits their applications as the incentives are not worth as compared to the loss in comfort levels for most time. Thus, a framework to comprehensively consider both operating costs and comfort levels is necessary. Moreover, distributed energy resources are widely deployed in commercial buildings such as roof-top solar panels, plug-in electric vehicles, and energy storage units, which bring various uncertainties to the distribution systems, i.e., (i) output of renewables; (ii) electricity prices; (iii) arrival and departure of plug-in electric vehicles; (iv) business hour demand response signals and (v) flexible energy demand. In this paper, we propose an optimal demand response framework to enable local control of demand-side appliances that are usually too small to participate in a retail electricity market. Several typical small demand side appliances, i.e., heating, ventilation, and air conditioning systems, electric water heaters and plug-in electric vehicles, are considered in our proposed model. Their operations are coordinated by a central controller, whose objective is to minimize the total cost and maximize the customers' comfort levels for multiple commercial buildings. A scenario-based stochastic programming is leveraged to handle the aforementioned uncertainties. Numerical results based on the practical data demonstrate the effectiveness of the proposed framework. In addition, the trade-off between the operation costs of commercial buildings and customers' comfort levels is illustrated.

show abstract

Section: Objective Function Related With Operating Costsmentioning

confidence: 99%

Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The physical space relationship between air conditioners and sensors was not considered. An example of ANNA can be found in Ahn et al, who proposed a comparative analysis of the controllers of heated air supply [34]. The analysis dealt with the amount of mass and air temperatures by using ANNA.…”

Section: Algorithms Of Model Predictive Controlmentioning

confidence: 99%

“…At present, the main MPC methods are Linear Regression Algorithm (LRA) [32], Time Series Prediction Algorithm (TSPA) [33], and Artificial Neural Network Algorithm (ANNA) [34]. Zhou et al analyzed LRA and nonlinear regression energy models based on performance counters and system utilization [32].…”

Section: Algorithms Of Model Predictive Controlmentioning

confidence: 99%

“…They aimed to control the mass and temperatures of supply air per users' demands. However, there was a lack of consideration of room temperature distribution in the learning model [34].…”

Section: Algorithms Of Model Predictive Controlmentioning

confidence: 99%

“…It is transformed from a linear exponential smoothing method to a non-linear quadratic polynomial exponential smoothing method [34]. It is ideal for describing the characteristics of nonlinear changes of loads and temperatures.…”

Section: Triple Esm Algorithm and Temperature Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

2017

View full text Add to dashboard Cite

Sustainable facilities management (SFM) opens the door of opportunity for companies to evaluate the quality of resources and environment management at their facilities. It enables the principles of sustainable development. There is still inefficiency in quantitative research of integrating environmental factors, particularly multi-source data, to monitor and control complicated systems in buildings. The objective of this research is to develop an effective method to dynamically optimize energy efficiency in SFM plans and strategies. The research question is: can the integrated proactive method reduce energy consumption with dynamically adjustable controls? This paper proposes a coordinated proactive control method using dynamic time-series prediction (PCM-DTSP) for SFM, which optimizes system controls by integrating the prediction results and monitored environmental-data. The results show that, after optimization, the temperature fluctuations are reduced to 33.3%. The average temperature and maximum temperature are reduced by 8% and 13.1%, respectively. The instantaneous power consumption was reduced by 0.17 KW per hour for each cooling system unit. The PCM-DTSP method can significantly optimize energy efficiency, which paves the way for long-term comprehensive energy management. The contribution of the research lies in its optimized control of energy consumption, temperature stabilization, and improvement of environmental comfort solutions, which can be generalized to various types of buildings.

show abstract

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Zhao

Ren

Gao

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.

show abstract

Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands

Cited by 60 publications

References 42 publications

Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market

Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Contact Info

Product

Resources

About