Article Open Access

Context-Aware Systems for Proactive Energy Efficiency Services

Maan Hameed, Nabaa Ahmed Noori, Aghaid Khudr Suleiman, Mahmood Jawad Abu-AlShaeer, Ahmed Sabah, M. Batumalay

Abstract


Static energy control systems are increasingly unable to meet the demands of modern built environments, where dynamic occupancy and fluctuating conditions lead to significant inefficiencies. This paper presents a context-aware system for proactive energy management that integrates real-time data acquisition, machine learning-based forecasting, and autonomous control. A multi-tiered architecture was developed and deployed across diverse settings residential, commercial, and industrial—to gather contextual data on temperature, occupancy, lighting, and equipment usage. The system uses predictive forecasting to anticipate short-term energy needs and reinforcement learning to optimize control strategies, ensuring both energy savings and user comfort. Results from the deployment demonstrate significant power reduction, high system responsiveness, and strong user satisfaction. Application-specific benchmarks revealed major efficiency gains in HVAC, lighting, and industrial machinery, while scalability tests confirmed stable performance under increasing sensor loads. This research validates the effectiveness of combining contextual intelligence with adaptive control to create sustainable, responsive, and human-centered energy systems. We provide a practical, modular framework for intelligent energy infrastructure in smart buildings and industrial parks. Future work will focus on enhancing model interpretability, integrating economic incentives, and exploring federated learning for distributed intelligence in support of energy efficiency.


Keywords


Context-Aware Systems, Energy Efficiency, Reinforcement Learning, Smart Buildings, Adaptive Control

References


Neves-Silva, R. and L.M. Camarinha-Matos Context-Based Decision Support System for Energy Efficiency in Industrial Plants. Sustainability, 2022. 14, DOI: 10.3390/su14073885.

Ali, M., et al., Intelligent energy management: Evolving developments, current challenges, and research directions for sustainable future. Journal of Cleaner Production, 2021. 314: p. 127904.

Shivaram, R., Z. Yang, and R.K. Jain, Context-aware Urban Energy Analytics (CUE-A): A framework to model relationships between building energy use and spatial proximity of urban systems. Sustainable Cities and Society, 2021. 72: p. 102978.

Ngwira, L., et al. Towards Context-Aware Smart Contracts for Blockchain IoT Systems. in 2021 International Conference on Information and Communication Technology Convergence (ICTC). 2021.

Riabchuk, V., et al., Utility-based context-aware multi-agent recommendation system for energy efficiency in residential buildings. Information Fusion, 2024. 112: p. 102559.

Bereketeab, L., et al., Energy Optimization in Sustainable Smart Environments with Machine Learning and Advanced Communications. IEEE Sensors Journal, 2024. 24(5): p. 5704-5712.

Deng, X., et al., Toward Smart Multizone HVAC Control by Combining Context-Aware System and Deep Reinforcement Learning. IEEE Internet of Things Journal, 2022. 9(21): p. 21010-21024.

Arevalo, J., et al. Cognitive Systems for the Energy Efficiency Industry. Energies, 2024. 17, DOI: 10.3390/en17081860.

Vale, Z., et al., Chapter 14 - Intelligent Energy-Oriented Home, in Artificial Intelligence to Solve Pervasive Internet of Things Issues, G. Kaur, P. Tomar, and M. Tanque, Editors. 2021, Academic Press. p. 269-289.

Shreenidhi, H.S., S. Upadhyay, and A. Gill. The Potential of Industrial Smart Grids in Optimizing Energy Consumption. in 2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT). 2024.

Chen, J., et al., SecRec: A Privacy-Preserving Method for the Context-Aware Recommendation System. IEEE Transactions on Dependable and Secure Computing, 2022. 19(5): p. 3168-3182.

L. Afuan and R. Rizal Isnanto, “Enhanced Fall Detection using Optimized Random Forest Classifier on Wearable Sensor Data,” Journal of Applied Data Sciences, vol. 6, no. 1, pp. 213–224, 2025, doi: 10.47738/jads.v6i1.498.

D. A. Dewi and T. B. Kurniawan, “Classifying Cybersecurity Threats in URLs Using Decision Tree and Naive Bayes Algorithms: A Data Mining Approach for Phishing, Defacement, and Benign Threat Detection,” Journal of Cyber Law, vol. 1, no. 2, pp. 175–189, 2025, doi: 10.63913/jcl.v1i2.10.

S. F. Pratama and P. A. Prastyo, “Evaluating Blockchain Adoption in Indonesia’s Supply Chain Management Sector,” Journal of Current Research in Blockchain, vol. 1, no. 3, pp. 190–213, 2024, doi: 10.47738/jcrb.v1i3.21.

J. Lin and Z. Shen, “Optimization of Data Encryption Technology in Computer Network Communication,” International Journal for Applied Information Management, vol. 3, no. 4, pp. 162–169, 2023, doi: 10.1088/1742-6596/2037/1/012070.

Vallarta-Serrano, S.I., et al. Integrated Sustainability Assessment Framework of Industry 4.0 from an Energy Systems Thinking Perspective: Bibliometric Analysis and Systematic Literature Review. Energies, 2023. 16, DOI: 10.3390/en16145440.

D. P. Lestari, A. Luthfi, C. Tama, S. Karlina, and A. Sultan, “Factors Affecting Information System Security: Information Security, Cyber Threats and Attacks, Physical Security, and Information Technology,” International Journal of Informatics and Information Systems, vol. 7, no. 1, pp. 16–21, 2024, doi: 10.47738/ijiis.v7i1.193.

S. F. Pratama, “Analyzing the Determinants of User Satisfaction and Continuous Usage Intention for Digital Banking Platform in Indonesia: A Structural Equation Modeling Approach,” Journal of Digital Market and Digital Currency, vol. 1, no. 3, pp. 267–285, 2024, doi: 10.47738/jdmdc.v1i3.21.

J. P. B. Saputra, and A. Kumar, “Emotion Detection in Railway Complaints Using Deep Learning and Transformer Models: A Data Mining Approach to Analyzing Public Sentiment on Twitter,” Journal of Digital Society, vol. 1, no. 2, pp. 1–14, 2025, doi: 10.63913/jds.v1i2.6.

Ahmed, M.A., et al. Toward an Intelligent Campus: IoT Platform for Remote Monitoring and Control of Smart Buildings. Sensors, 2022. 22, DOI: 10.3390/s22239045.

Sierla, S., H. Ihasalo, and V. Vyatkin A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems. Energies, 2022. 15, DOI: 10.3390/en15103526.

A. D. Buchdadi and A. S. M. Al-Rawahna, “Anomaly Detection in Open Metaverse Blockchain Transactions Using Isolation Forest and Autoencoder Neural Networks,” International Journal Research on Metaverse, vol. 2, no. 1, pp. 24–51, 2025, doi: 10.47738/ijrm.v2i1.20.

Lekidis, A. and E.I. Papageorgiou Edge-Based Short-Term Energy Demand Prediction. Energies, 2023. 16, DOI: 10.3390/en16145435.

Sarker, M., et al., Energy demand forecasting of remote areas using linear regression and inverse matrix analysis. International Journal of Electrical and Computer Engineering (IJECE), 2024. 14: p. 129-139.

E. D. Lusiana, S. Astutik, Nurjannah, and A. B. Sambah, “Using Machine Learning Approach to Cluster Marine Environmental Features of Lesser Sunda Island,” Journal of Applied Data Sciences, vol. 6, no. 1, pp. 247–258, 2025, doi: 10.47738/jads.v6i1.478.

Peng, Y., et al., Energy Consumption Optimization for Heating, Ventilation and Air Conditioning Systems Based on Deep Reinforcement Learning. IEEE Access, 2023. 11: p. 88265-88277.

Lazaridis, C.R., et al. Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management. Energies, 2024. , DOI: 10.3390/en17030581.

C. R. A. Widiawati, Sarmini, and D. Yuliana, “Predicting Network Performance Degradation in Wireless and Ethernet Connections Using Gradient Boosting, Logistic Regression, and Multi-Layer Perceptron Models,” Journal of Applied Data Sciences, vol. 6, no. 1, pp. 325–338, 2025, doi: 10.47738/jads.v6i1.519.

Campagna, L.M. and F. Fiorito on the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis. Energies, 2022. 15, DOI: 10.3390/en15010354.

A. B. Prasetio, M. Aboobaider, and A. Ahmad, “Machine Learning for Wage Growth Prediction: Analyzing the Role of Experience, Education, and Union Membership in Workforce Earnings Using Gradient Boosting,” Artificial Intelligence in Learning, vol. 1, no. 2, pp. 153–172, 2025, doi: 10.63913/ail.v1i2.12.

Arnob, S.S., et al., Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries. IEEE Access, 2023. 11: p. 39751-39775.

Im, J., et al., Data pipeline for real-time energy consumption data management and prediction. Frontiers in Big Data, 2024. Volume 7 - 2024.

Hou, S., et al., Real-Time Energy Management Strategy of a Fuel Cell Electric Vehicle with Global Optimal Learning. IEEE Transactions on Transportation Electrification, 2023. 9(4): p. 5085-5097.

Bilésimo, T.L. and E. Ghisi Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users. Sustainability, 2024. 16, DOI: 10.3390/su16104258.

Gheorghiu, C., et al. A Novel Methodology for Developing an Advanced Energy-Management System. Energies, 2024. 17, DOI: 10.3390/en17071605.

Ishaq, K. and S.S. Farooq, Exploring iot in smart cities: Practices, challenges and way forward. arXiv preprint arXiv:2309.12344, 2023.

W. Y. Leong, "Digital Technology for ASEAN Energy," 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT), August 2023, pp. 1480-1486. DOI: 10.1109/ICCPCT58313.2023.10244806




DOI: https://doi.org/10.52088/ijesty.v5i1.1728

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Copyright (c) 2025 Maan Hameed, Nabaa Ahmed Noori, Aghaid Khudr Suleiman, Mahmood Jawad Abu-AlShaeer, Ahmed Sabah, M. Batumalay

International Journal of Engineering, Science, and Information Technology (IJESTY) eISSN 2775-2674