CO2 Concentration Forecasting in an Office Using Artificial Neural Network

Uncertainty is the state of all operation, components, and objective environment that makes impossible to describe the existing state. Forecasting techniques are essential in the field of knowledge development to overcome the uncertainty to increase the efficiency of all systems. In this paper, artificial neural network algorithm is applied to forecast the CO2 concentration in an office building. The algorithm is implemented in Rstudio software using neural net package. The case study of the paper presents two scenarios with different input data to propose the impacts of train data on forecasting algorithms results. The used dataset in the case study is real data that have been monitored for 2 years. The obtained results of algorithms show the predicted values of CO2 concentration in one office for 600 minutes of a working day. The mean percentage error means absolute percentage error, and standard deviation of predicted data for both scenarios are presented in results section.

[1]  Reza Shirmohammadi,et al.  Forecasting of CO2 emissions in Iran based on time series and regression analysis , 2019, Energy Reports.

[2]  Juan M. Corchado,et al.  Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management , 2017 .

[3]  Yan Chen,et al.  Stock Market Trend Prediction Using High-Order Information of Time Series , 2019, IEEE Access.

[4]  Air conditioner consumption optimization in an office building considering user comfort , 2020 .

[5]  Steve Bonino.  Carbon Dioxide Detection and Indoor Air Quality Control. , 2016, Occupational health & safety.

[6]  Isabel Praça,et al.  Day ahead electricity consumption forecasting with MOGUL learning model , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[7]  Mahsa Khorram,et al.  Office building participation in demand response programs supported by intelligent lighting management , 2018 .

[8]  Isabel Praça,et al.  Application of a Hybrid Neural Fuzzy Inference System to Forecast Solar Intensity , 2016, 2016 27th International Workshop on Database and Expert Systems Applications (DEXA).

[9]  Jianchun Peng,et al.  A review of deep learning for renewable energy forecasting , 2019, Energy Conversion and Management.

[10]  Lei Shi,et al.  Application of Artificial Neural Network to Predict the Hourly Cooling Load of an Office Building , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[11]  B. Sivaneasan,et al.  Solar Forecasting using ANN with Fuzzy Logic Pre-processing , 2017 .

[12]  Giuliano Resce,et al.  Beyond CO2: A multi-criteria analysis of air pollution in Europe , 2019, Journal of Cleaner Production.

[13]  Taher Niknam,et al.  Investigation of Carrier Demand Response Uncertainty on Energy Flow of Renewable-Based Integrated Electricity–Gas–Heat Systems , 2018, IEEE Transactions on Industrial Informatics.

[14]  Lei Xu,et al.  Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load , 2019, Applied Energy.