An Integrated Energy and Environmental Monitoring System for Community-Scale Cricket Farming via Home Assistant

Main Article Content

Panuwit Puttaraksa
Sarawut Polvongsri
Thanyaluck Sundach
Chawaroj Jaisin
Sulucksana Mongkong

Abstract

Cricket farming in semi-closed systems requires maintaining an optimal temperature range, which typically demands high electricity consumption and increases production costs. Moreover, low-cost monitoring sensors used in agriculture have rarely been calibrated against standard instruments, and the integration of renewable energy with intelligent control systems for insect farming remains limited. This study presents a temperature monitoring and control system for cricket farms powered by a 4.4 kWp solar photovoltaic–thermal hybrid system (Solar PVT) and 10.8 kWth heat pump, designed to maintain the temperature of 20 rearing bins within 28 - 30 oC. The control system was developed on the Home Assistant platform with a Tuya IoT module, which effectively regulates the temperature according to predefined conditions, ensuring stable operation throughout the rearing process. Experimental results demonstrated that the Tuya IoT sensors achieved high accuracy (R2 > 0.99,
RMSE 0.496 oC, 0.0295 kW, and MAPE <2%), confirming the system’s reliability. Furthermore, the system reduced grid electricity consumption by 41.5 %. Additional analysis indicated an annual electricity generation of 7,570.63 kWh, a reduction in greenhouse gas emissions of 3,784.56 kgCO2eq/year, and a payback period of 5.5 years. These findings highlight the system’s technical, economic, and environmental feasibility for adoption in community-scale cricket farms.

Article Details

How to Cite
[1]
P. Puttaraksa, S. Polvongsri, T. Sundach, C. Jaisin, and S. Mongkong, “An Integrated Energy and Environmental Monitoring System for Community-Scale Cricket Farming via Home Assistant”, RMUTI Journal, vol. 19, no. 1, pp. 56–73, Apr. 2026.
Section
Research article
Author Biographies

Panuwit Puttaraksa, School of Renewable Energy, Maejo University

Ph.D student, Program in Renewable Energy Engineering,

School of Renewable Energy, Maejo University

Sarawut Polvongsri, School of Renewable Energy, Maejo University

Asst. Prof. Dr. Sarawut Polvongsri Affiliated with: School of renewable energy Maejo university

Thanyaluck Sundach, School of Renewable Energy, Maejo University

miss thanyaluck sundach Researcher Affiliated with: School of renewable energy Maejo university

Chawaroj Jaisin, School of Renewable Energy, Maejo University

Assoc. Prof. Dr. Chawaroj Jaisin Affiliated with: School of renewable energy Maejo university

Sulucksana Mongkong, School of Renewable Energy, Maejo University

Asst. Prof. Dr. Sulaksana Mongkon Affiliated with: School of renewable energy Maejo university

References

Aavild, A.P., Rosenkrantz de Lasson, A., Moesgaard Andersen, C., Christensen, E., Moreschini, S., Hästbacka, D., Taibi, D. and Albano, M. (2024). Distributed Home Automation with Home Assistant. In Peltonen, E., Hyrynsalmi, S., Wagner, I., Rellermeyer, J. and Mohan, N. (Eds.), IoT '24: Proceedings of the 14th International Conference on the Internet of Things (pp. 206-212). https://doi.org/10.1145/3703790.3703828

Anal, S. and Kaur, S. (2024). Live Data Monitoring in Industry. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 12(VI), 304-313. https://doi.org/10.22214/ijraset.2024.63080

Araújo, T., Silva, L. and Moreira, A. (2020). Evaluation of Low-Cost Sensors for Weather and Carbon Dioxide Monitoring in Internet of Things Context. IoT, 1(2), 286-308. https://doi.org/10.3390/iot1020017

Bar-Gill, S., Brynjolfsson, E. and Hak, N. (2024). Helping Small Businesses Become More Data-Driven: A FieldExperiment on eBay. Management Science, 70(11), 7345-7372. http://doi.org/10.2139/ssrn.4400425

G, K., Ediga, P., S, A., P, A., T, S., Mittal, A., Rajvanshi, S. and Habelalmateen, M.I. (2024). Smart Energy Management: Real-Time Prediction and Optimization for IoT-Enabled Smart Homes. Cogent Engineering, 11(1). https://doi.org/10.1080/23311916.2024.2390674

Hassan, S.I., Alam, M.M., Illahi, U., Al Ghamdi, M.A., Almotiri, S.H., and Su’ud, M.M. (2021). A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture. IEEE Access, 9, 32517-32548. https://doi.org/10.1109/ACCESS.2021.3057865

Huang, H., Yi, J., Gao, J., Liu, P., Meng, L., and Zou, H. (2023). Design of Smart Laboratory System Based on Home Assistant. 14th International Conference on Mechanical and Aerospace Engineering (ICMAE), 575-580. https://doi.org/10.1109/ICMAE59650.2023.10424532.

Islam, M.T., Azad, M.S., Ahammed, M.S., Rahman, M.W., Azad, M.M., Nasir, M.K. (2022). IoT Enabled Virtual Home Assistant Using Raspberry Pi. In Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (Eds.), Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, Vol. 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_52

Koponen, P., Ikäheimo, J., Koskela, J., Brester, C. and Niska, H. (2020). Assessing and Comparing Short Term Load Forecasting Performance. Energies, 13(8), https://doi.org/10.3390/en13082054

Maity, A.K., Darshan, D. and Chauhan, K. (2024, November). IoT-Based Industrial Equipment Monitoring System: Revolutionizing Maintenance through Smart Data Analytics. 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024). https://doi.org/10.2139/ssrn.5065523

Mansattha, M., Dao, H. and Jikaraji, A. (2023). Smart Meter Design for Energy Consumption Monitoring of Residential Premises. Journal of Applied Research on Science and Technology (JARST), 22(2), 250745-250745. https://ph01.tci-thaijo.org/index.php/rmutt-journal/article/view/250745/170681

Moon, K.A., Kim, S.B., Choi, H.U. and Choi, K.H. (2024). Experimental Study on the Heat Pump Performance Combined with Dual-Purpose Solar Collector. Energies, 17(12). https://doi.org/10.3390/en17123038

Naidu, G.G.S., Patnaik, R.K., Kumar, R.Y. and Viswas, N. (2024). An Analysis of Energy Monitoring Solutions for Smart Home Applications. International Research Journal of Modernization in Engineering Technology and Science, 6(11), 5581-5588. https://www.irjmets.com/uploadedfiles/paper/issue_11_november_2024/64692/final/fin_irjmets1732986336.pdf

Natale, C., Dongellini, M., Naldi, C. and Morini, G.L. (2025). Evaluation of the Seasonal Energy Performance of a Dual-Source Heat Pump Through Dynamic Experimental Tests. Energies, 18(10). https://doi.org/10.3390/en18102532

Omia, E., Bae, H., Park, E., Kim, M.S., Baek, I., Kabenge, I. and Cho, B.K. (2023). Remote Sensing in Field Crop Monitoring: A Comprehensive Review of Sensor Systems, Data Analyses and Recent Advances. Remote Sensing, 15(2). https://doi.org/10.3390/rs15020354

Phaphan, W. and Phutthamat, W. (2023). The Comparison of Forecasting Modelsfor Total Premiums of Life Insurance Companies inThailand. Huachiew Chalermprakiet Science and Technology Journal, 9(2), 64-74. https://ph02.tci-thaijo.org/index.php/scihcu/article/view/249738/169825

Salee, S., Wannaphrom, W., Ngandee, A., Krudngern, S., Thongpron, J. and Maungjai, W. (2020). The Monitoring for Control and Analysis of Electrical Energy used in Agricultural Production. Case Study of Product Building, Thung Luang Royal Project Foundation. Journal of Agricultural Technology Research, 3(1), 77-90. https://so04.tci-thaijo.org/index.php/JIT/article/view/242010

Siswoyo, A. (2025). Optimization of Temperature Sensor Selection for Incubators: Real-Time Accuracy Analysis of DHT22, LM35, and DS18B20 in Controlled Environment Simulations. Internet of Things and Artificial Intelligence Journal, 5(1). https://doi.org/10.31763/iota.v5i1.877

Suwannapong, C. and Chaiyong, W. (2015). The Application of Wireless Sensor Network to Monitor and Identify the Position of Beef Cattle in the Livestock Production. Farm Engineering and Automation Technology Journal, 1(1), 15-21. https://ph02.tci-thaijo.org/index.php/featkku/article/view/175993

Suwannahong, P. (2018). Dashboard Design for Data Analysis of Sale Promotion: A Case Study of a Commercial Bank [Doctoral dissertation, Thammasat University]. Thammasat University Library. https://ethesisarchive.library.tu.ac.th/thesis/2018/TU_2018_6023036137_9545_10069.pdf

Thaipreecha, W. and Puntusavase, K. (2023). Applying Dashboards for Manufacturing by Comparing Programs Used Between Google Data Studio and Microsoft Power BI. Journal of Management Science Research, Surindra Rajabhat University, 7(3), 39-53. https://so02.tci-thaijo.org/index.php/jmsr/article/view/258426

Verma, Y., Verma, A., Chatterjee, S. and Sagar, P. (2024). Remote Sensing Applications in Agriculture. In Doggalli, G., Modi, R., Mistry, D.Y., Rathwa, M.K., and Mali, P.D. (Eds.), Recent Trends in Agriculture Vol. 13. (pp. 159-180). Integrated Publications.

Wanchupela, N. and Polvongsri, S. (2021). Size and Type Optimization of Solar Photovoltaic Thermal Hybrid Assisted Heat Pump in Slaughterhouse. Engineering Journal of Chiang Mai University, 28(1), 127-140. https://ph01.tci-thaijo.org/index.php/EngJCMU/article/view/244072/166124

Zhang, X., Zhang, T., Young, A.A. and Li, X. (2014). Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data. Plos one, 9(2). https://doi.org/10.1371/journal.pone.0088075