Machine Learning Driven Optimisation of Energy Efficiency and Indoor Air Quality in Educational Buildings
Educational buildings breathe life into growing minds, yet they often struggle to balance two essential needs: conserving energy and maintaining healthy indoor air quality. The study behind this work steps into that tension, exploring how traditional HVAC control methods fall short when faced with shifting indoor conditions and rising sustainability pressures. By pairing experimental testing with advanced machine learning models, the research aims to create smarter, adaptive systems that reduce energy consumption while protecting occupant health. This introduction frames the need for innovation, outlining why educational spaces demand data-driven solutions capable of reacting in real time.
Machine Learning Integration for HVAC Optimization
The research explores how models such as RNN, LSTM, GRU, and CNN can interpret vast streams of environmental data and turn them into actionable predictions. With over 35,000 real-world records, the study demonstrates how these models learn patterns in CO2 concentration, particulate levels, temperature, humidity and even external factors like time, date, and rainfall. By processing these dynamic interactions, machine learning becomes a guiding system that supports more efficient HVAC operation, reduces waste, and strengthens environmental resilience in educational settings.
Experimental Evaluation of IAQ and Energy Parameters
This section highlights the experimental backbone of the study, where indoor air quality indicators were tracked and analyzed to understand their relationship with HVAC behavior. CO2 buildup, temperature shifts, humidity changes, and particulate spikes were monitored to capture how learning environments respond to occupancy and weather. The findings show the delicate balance between energy consumption and IAQ, reinforcing the value of real-time data for system optimization and resource management.
Model Performance and Predictive Accuracy
The research reports high predictive accuracy, with models surpassing 92 percent performance in forecasting IAQ and energy behavior. GRU and LSTM architectures stood out due to their ability to capture long-term dependencies and temporal trends within the dataset. Their strength in recognizing subtle environmental fluctuations allows HVAC systems to adjust proactively rather than reactively, ensuring stable indoor conditions while minimizing unnecessary energy use.
Interpretability and SHAP-Based Insights
Beyond prediction, the study integrates SHAP values to explain why models make certain decisions. This interpretability framework reveals the influence of key features, showing how CO2, particulate matter, humidity, and external variables shape system output. By offering transparent reasoning behind the algorithms, the research empowers facility managers and policymakers to trust automated control strategies and adopt them with confidence.
Scalability and Impact on Sustainable Educational Infrastructure
The final topic focuses on the broader impact and scalability of the approach. When applied across multiple educational buildings, the ML-driven strategy shows strong potential to reduce energy costs, enhance occupant well-being, and support low-carbon goals. Its adaptability to different climates and building types demonstrates its long-term value, positioning it as a powerful step toward smarter, healthier, and more resilient learning environments.
Architecture Engineers Awards
🔗 Nominate now! 👉 https://architectureengineers.com/award-nomination/?ecategory=Awards&rcategory=Awardee
🌐 Visit: architectureengineers.com
📩 Contact: contact@architectureengineers.com
Get Connected Here:
*****************
Instagram : https://www.instagram.com/architecture_engineers_awards/
Facebook : https://www.facebook.com/profile.php?id=61576995475934
Tumblr : https://www.tumblr.com/blog/architectureengineers
Pinterest : https://in.pinterest.com/researcherawards123/
Blogger : https://architectureengineers.blogspot.com/
Twitter : https://twitter.com/
#energyefficiency
#indoorairquality
#machinelearning
#HVACoptimisation
#LSTM
#GRU
#RNN
#CNN
#IAQmonitoring
#sustainablebuildings
#educationinfrastructure
#smartHVAC
#realtimeprediction
#datadrivencontrol
#environmentalanalytics
#carbonreduction
#predictivemodelling
#buildingperformance
#AIineducation
#energymanagement
Comments
Post a Comment