Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems

Energies, 2023

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/en16083508
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

An appropriate microclimate is one of the most important factors of a healthy and comfortable life. The microclimate of a place is determined by the temperature, humidity and speed of the air. Those factors determine how a person feels thermal comfort and, therefore, they play an essential role in people’s lives. Control of microclimate parameters is a very important topic for buildings, as well as greenhouses, where adequate microclimate is fundamental for best-growing results. Microclimate systems require adequate monitoring and maintenance, for their failure or suboptimal performance can increase energy consumption and have catastrophic results. In recent years, Fault Detection and Diagnosis in microclimate systems have been paid more attention. The main goal of those systems is to effectively detect faults and accurately isolate them to a failing component in the shortest time possible. Sometimes it is even possible to predict and anticipate failures, which allows preventing the failures from happening if appropriate measures are taken in time. The present paper reviews the state of the art in fault detection and diagnosis methods. It shows the growing importance of the topic and highlights important open research questions.