Abstract
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%.
Highlights
According to the International Energy Agency (IEA) report titled “The Future of Cooling”published in 2018 [1], the global amount of air conditioners in buildings will grow to 5.6 billion by 2050, up from 1.6 billion today, which amounts to 10 new air conditioners sold every second for the 30 years
Based on the above surveyed results, this study proposes and develops cloud-based artificial intelligence (AI), and its energy saving effect is expected to increase on the basis of variable speed drives (VSDs) given the Energies 2020, 13, x FOR PEER REVIEW
This study investigates 9 items: artificial neural network (ANN); decision-making system (DMS); fuzzy, generic algorithm (GA); multi-agent system (MAS); machine learning (ML); model-based predictive control (MPC); rule-based reasoning (RBR); recurrent neural network (RNN); and spiking neural network (SNN)
Summary
According to the International Energy Agency (IEA) report titled “The Future of Cooling”published in 2018 [1], the global amount of air conditioners in buildings will grow to 5.6 billion by 2050, up from 1.6 billion today, which amounts to 10 new air conditioners sold every second for the 30 years. Global energy demand from air conditioners is expected to triple by 2050. Energies 2020, 13, 2001 air conditioners results in large costs and environmental implications. The IEA report indicated that increased cooling demands will be crucial in hot regions of the world. This problem is sensitive in fast-growing nations, such as India, where the share of AC in peak electricity load may reach 45% in 2050, if no actions are taken. This will require large investments in new power plants to meet peak power demand
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