scholarly journals Artificial Intelligence Based Air Conditioner Energy Saving Using a Novel Preference Map

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 206622-206637
Author(s):  
Ramasamy Kannan ◽  
Manasij Sur Roy ◽  
Sai Harish Pathuri
Keyword(s):  
Artificial Intelligence ◽  
Energy Saving ◽  
Air Conditioner ◽  
Preference Map

scholarly journals Air Conditioning Energy Saving from Cloud-Based Artificial Intelligence: Case Study of a Split-Type Air Conditioner

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2001 ◽  
Author(s):  
Dasheng Lee ◽  
Fu-Po Tsai
Keyword(s):  
Artificial Intelligence ◽  
Energy Efficiency ◽  
Energy Saving ◽  
Pid Control ◽  
Air Conditioning ◽  
Efficiency Index ◽  
Air Conditioner ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Air Conditioners

This study developed cloud-based artificial intelligence (AI) that could run AI programs in the cloud and control air conditioners remotely from home. AI programs in the cloud can be altered any time to provide good control performances without altering the control hardware. The air conditioner costs and prices can thus be reduced by the increasing energy efficiency. Cloud control increased energy efficiency through AI control based on two conditions: (1) a constant indoor cooling rate and (2) a fixed stable range of indoor temperature control. However, if the two conditions cannot be guaranteed or the cloud signals are lost, the original proportional-integral-differential (PID) control equipped in the air conditioner can be used to ensure that the air conditioner works stably. The split-type air conditioner tested in this study is ranked eighth among 1177 air conditioners sold in Taiwan according to public data. It has extremely high energy efficiency, and using AI to increase its energy efficiency was challenging. Thus, this study analyzed the literature of AI-assisted controls since 1995 and applied it to heating, ventilation, and air conditioning equipment. Two technologies with the highest energy saving efficiency, a fuzzy + PID and model-based predictive control (MPC), were chosen to be developed into two control methodologies of cloud-based AI. They were tested for whether they could improve air conditioning energy efficiency. Energy efficiency measurement involved an enthalpy differential test chamber. The two indices, namely the energy efficiency ratio (EER) and cooling season power factor (CSPF), were tested. The EER measurement is the total efficiency value obtained when testing the required electric power at the maximum cooling capacity under constantly controlled temperature and humidity. CSPF is the tested efficiency value under dynamic conditions from changing indoor and outdoor temperatures and humidity according to the climate conditions in Taiwan. By using the static energy efficiency index EER for evaluation, the fuzzy + PID control could not save energy, but MPC increased the EER value by 9.12%. By using the dynamic energy efficiency index CSPF for evaluation, the fuzzy + PID control could increase CSPF by 3.46%, and MPC could increase energy efficiency by 7.37%.

scholarly journals Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring

2018 ◽  
Vol 7 (2) ◽  
pp. 143-152
Author(s):  
Khairuddin Khalid ◽  
Azah Mohamed ◽  
Ramizi Mohamed ◽  
Hussain Shareef
Keyword(s):  
Artificial Intelligence ◽  
Reactive Power ◽  
Performance Comparison ◽  
Support Vector ◽  
Fluorescent Light ◽  
Time Interval ◽  
Smart Meter ◽  
Air Conditioner ◽  
Artificial Intelligence Techniques ◽  
Load Monitoring

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

An Implementation of Demand Side Energy Management for Refined Load Control

2015 ◽  
Vol 1092-1093 ◽  
pp. 409-413
Author(s):  
Xiao Ming Jin ◽  
Xue Lin Zhao ◽  
Kun Qi Jia ◽  
Guang Yu He
Keyword(s):  
Energy Saving ◽  
Energy Management ◽  
Energy Demand ◽  
Air Conditioner ◽  
Demand Side ◽  
Zigbee Network ◽  
Remote Controller ◽  
Cost Efficient ◽  
End Use ◽  
And Control

Demand Side Energy Management System (DSEMS) manages energy demand by controlling end-use appliances in a refined, energy-saving, cost-efficient and user-friendly way. The DSEMS runs on an Android tablet computer, which serves as energy gateway to communicate with two types of controllers via ZigBee network. Smart sockets connected to the ZigBee network will monitor and control plug-in loads and IR remote controller (IRRC) for air-conditioner temperature setting. The proposed system has been installed in an apartment with over 24 rooms as a paradigm, which proved that the DSEMS can realize the autonomous energy-saving via real-time surveillance and control on household appliances.

Development of Multi-functional Sensor Module for Energy Saving Air Conditioner System

2012 ◽  
Author(s):  
Shinji Ueki ◽  
Hiroshi Imamoto ◽  
Koji Ando ◽  
Tsukasa Fujimori ◽  
Susumu Sugiyama ◽  
...  
Keyword(s):  
Energy Saving ◽  
Air Conditioner ◽  
Sensor Module

Studies on the Heat-Resistance and Energy-Saving of the Eco-Roof in Southern Urban Districts

2012 ◽  
Vol 209-211 ◽  
pp. 1862-1866
Author(s):  
Sai Jun Zhou ◽  
Bo Zhi Ren ◽  
Chang Su
Keyword(s):  
Heat Resistance ◽  
Thermal Property ◽  
Energy Saving ◽  
Green Roof ◽  
Air Conditioner ◽  
Contrastive Study ◽  
Urban Districts ◽  
The Common

Roof is one of the main structures of a building, and it plays an important role in improving the degree of indoor thermal conformability so as to reduce the using of air-conditioner. Through a contrastive study on eco-roof and the common roof in a certain living district in Xiangtan City, the article comes to the conclusion: the water-storing green roof can enhance the urban eco-environment, modulate the indoor thermal property and decrease the using of air-conditioner.

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