Affiliation:
1. Department of Electronics Engineering, Kookmin University, Seoul, Republic of Korea
Abstract
Adequate air ventilation systems and maintaining environmental air quality are essential things that need to be considered to create an excellent industrial environment in a company. Operating or maintaining ecological air quality is a challenge for companies to optimize the existing work environment. Especially at the economic and business level in the company are facing the main problems. In this case, monitoring and predicting future air quality are needed to maintain air quality conditions. PM10 concentration, humidity, and temperature were used to predict annual and seasonal indoor air quality using a recurrent neural network (RNN) and long short-term memory (LSTM). In this paper, we propose the IAQP method for air quality management systems that combines indoor air quality forecasting based on real-time data. To measure indoor air quality, we predict the outcome from the IIoT sensor and LoRa sensor. The result of prediction is that the multilevel RNN model outperformed the LSTM, as the model demonstrated excellent results and feasibility.
Funder
Ministry of Science, ICT and Future Planning
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Cited by
8 articles.
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