Using Artificial Neural Network to Execute Demand Response Considering Indoor Thermal Comfort and Forecast Load-Shedding

2014 ◽  
Vol 716-717 ◽  
pp. 1399-1408
Author(s):  
Yung Chung Chang ◽  
Jyun Ting Lu ◽  
Yu Chung Liu ◽  
Chun Hong Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Comfort ◽  
Demand Response ◽  
Load Shedding ◽  
Cooling Load ◽  
Air Conditioning System ◽  
Indoor Thermal Comfort ◽  
Central Air Conditioning ◽  
Artificial Neural

This paper used artificial neural network to forecast the cooling load in the building in 24 hours. The unloading experiment kept the indoor thermal comfort at the ideal range of PMV=0~0.5 and PPD=5~10. Finally, dry bulb temperature, relative humidity, wet-bulb temperature and forecast cooling load were used for modeling by neural network. We can use this model to forecast how much load can be unloaded in summer peak hours accurately. This method controls the demand response for central air conditioning system, not only maintaining comfortable indoor environment, but also attaining the goals for reducing the electric expenses.

scholarly journals Utilizing Artificial Neural Network for Prediction of Occupants Thermal Comfort: A Case Study of a Test Room Fitted With a Thermoelectric Air-Conditioning System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 99709-99728 ◽  
Author(s):  
Kashif Irshad ◽  
Asif Irshad Khan ◽  
Sayed Ameenuddin Irfan ◽  
Md. Mottahir Alam ◽  
Abdulmohsen Almalawi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Comfort ◽  
Air Conditioning ◽  
Air Conditioning System ◽  
Test Room ◽  
Artificial Neural

scholarly journals A NILM method for cooling load disaggregation based on artificial neural network

2019 ◽  
Vol 111 ◽  
pp. 05020 ◽  
Author(s):  
Ziwei Xiao ◽  
Jiaqi Yuan ◽  
Wenjie Gang ◽  
Chong Zhang ◽  
Xinhua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Management ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Building Energy Management ◽  
Load Disaggregation ◽  
Artificial Neural ◽  
Cooling Loads

The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.

Application of Artificial Neural Network for Modeling of Mechanical Cooling Tower

2011 ◽  
Vol 383-390 ◽  
pp. 7746-7749 ◽  
Author(s):  
Wei Shun Huang ◽  
Ching Wei Chen ◽  
Cheng Wen Lee ◽  
Ching Liang Chen ◽  
Tien Shuen Jan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Flow ◽  
Cooling Tower ◽  
Air Flow ◽  
Cooling Water ◽  
Computation Method ◽  
Cooling Load ◽  
Artificial Neural ◽  
Tower Model

The objective of the study is to focus on the application of the artificial neural network to configure a heat-radiating model for cooling towers within the parameters of fluctuating in air flow or cooling water flow. To achieve the objective, a cooling tower heat balancing equation have been used to instill the correlations between a cooling tower cooling load to the four predefined parameters. Based on the premise established, the parameters of a cooling tower’s air flow and cooling water flow in a modulated process are utilized in an experimental system for collecting relevant operating data. Lastly, the artificial neural network tool derived from the Matlab software is utilized to define the input parameters being – the cooling water temperature, ambient web-bulb temperature, cooling tower air flow, and cooling water flow, with an objective set to instilling a cooling tower model for defining a cooling tower cooling load. In addition, the tested figures are compared to the simulated figures for verifying the cooling tower model. By utilizing the method derived from the model, the mean error of between 0.72 and 2.13% is obtained, with R2 value rated at between 0.97 and 0.99. The experiment findings show a relatively high reliability that can be achieved for configuring a model by using the artificial neural network. With the support of an optimized computation method, the model can be applied as an optimization operating strategy for an air-conditioning system’s cooling water loop.

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