IoT Solution Approach for Energy Consumption Reduction in Buildings: Part 4. Mathematical Model and Experiments for Cooling Energy Consumption
            
            2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2019): Conference Proceedings
            2019
            
        
                Ansis Avotiņš,
        
                Andrejs Podgornovs,
        
                Pēteris Apse-Apsītis,
        
                Armands Šenfelds,
        
                Egīls Dzelzītis,
        
                Kristaps Zadeiks
        
    
            
            
            Nowadays it is possible to obtain almost real-time measurement data using various IoT solutions, which can be used in order to control building management systems like heating, ventilation, cooling equipment (chiller), lighting. Nevertheless there are limited number of solutions allowing to control it by using hourly data (like electrical power consumption, room temperatures, humidity, CO2 levels, heat energy, ventilation system pressures, outdoor climate data). This paper deals with 6R2C mathematical model development, that uses real-time data obtained from IoT sensors, practical measurements and
experimental testing results achieved during summer period, when the cooling energy is needed. Measurements and experiments were conducted for certain building zone, which is PN4 ventilation zone for the most electrical energy consuming HVAC system of the building, located also in the south side and
having most impact by the sun radiation. Using simplified modeling and input data approach, CV(RMSE) estimation of the model for daily consumption for the period from 8 August to 8 September resulted in a value of 28.62%. In monthly period average energy consumption error (single month) is 0.14%.
            
            
            
                Keywords
                energy efficiency, energy consumption, Internet of Things, 6R2C, building simulation, building cooling load prediction
            
            
                DOI
                10.1109/RTUCON48111.2019.8982307
            
            
                Hyperlink
                https://ieeexplore.ieee.org/document/8982307
            
            
            Avotiņš, A., Podgornovs, A., Apse-Apsītis, P., Šenfelds, A., Dzelzītis, E., Zadeiks, K. IoT Solution Approach for Energy Consumption Reduction in Buildings: Part 4. Mathematical Model and Experiments for Cooling Energy Consumption. In: 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2019): Conference Proceedings, Latvia, Rīga, 7-9 October, 2019. Piscataway, NJ: IEEE, 2019, pp.388-394. ISBN 978-1-7281-3943-2. e-ISBN 978-1-7281-3942-5. Available from: doi:10.1109/RTUCON48111.2019.8982307
            
                Publication language
                English (en)