Sarahana Shrestha
Skip Abstract Section
Abstract
The overconsumption of energy in recent times has motivated many studies. Some of these explore the application of web technologies and machine learning models, aiming to increase energy efficiency and reduce the carbon footprint. This paper aims to review three areas that overlap between the web and energy usage in the commercial sector: IoT (Internet of Things), cloud computing and opinion mining. The paper elaborates on problems in terms of their causes, influences, and potential solutions, as found in multiple studies across these areas; and intends to identify potential gaps with the scope for further research. In the rapidly digitizing and automated world, these three areas can offer much contribution towards reducing energy consumption and making the commercial sector more energy efficient. IoT and smart manufacturing can assist much in effective production, and more efficient technologies as per energy usage. Cloud computing, with reference to its impact on green IT (information technology), is a major area that contributes towards the mitigation of carbon footprint and the reduction of costs on energy consumption. Opinion mining is significant as per the part it plays in understanding the feelings, requirements and demands of the consumers of energy as well as the related stakeholders, so as to help create more suitable policies and hence navigate towards more energy efficient strategies. This paper offers comprehensive analyses on the literature in the concerned areas to fathom the current status and explore future possibilities of research across these areas and the related multidisciplinary avenues.
- Accenture. 2022. Accelerating global companies toward net zero by 2050. Accessed via https://www.accenture.com/content/dam/accenture/final/capabilities/strategy-and-consulting/strategy/document/Accenture-Net-Zero-By-2050-Global-Report-2022.pdfGoogle Scholar
- Afzal, B.; Umair, M.; Shah, G.A.; Ahmed, E. 2019. Enabling IoT platforms for social IoT applications: Vision, feature mapping, and challenges. Future Gener. Comput. Syst. 92, 718--731.Google ScholarDigital Library
- Agostini, L.; Filippini, R. 2019. Organizational and managerial challenges in the path toward Industry 4.0. Eur. J. Innov. Manag. 22, 406--421.Google ScholarCross Ref
- Bates, R. W., and Moore, E. A. 1992. Commercial energy efficiency and the environment (No. 972). The World Bank.Google Scholar
- Bermeo-Ayerbe, M. A., Ocampo-Martinez, C., & Diaz-Rozo, J. 2022. Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems. Energy, 238, 121691.Google ScholarCross Ref
- Chang, W.; Ellinger, A.E.; Kim, K.; Franke, G.R. 2016. Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors. Eur. Manag. J., 34, 282--295.Google ScholarCross Ref
- Chen, Z., Jiang, C., and Xie, L. 2018. Building occupancy estimation and detection: a review. Energy Build, 169, 260--270.Google ScholarCross Ref
- Conti, C.J. Varde A., and Wang, W. 2022. Human-Robot Collaboration with Commonsense Reasoning in Smart Manufacturing Contexts. IEEE Transactions on Automation Science and Engineering (IEEE TASE journal), 19(3): 1784--1797.Google ScholarCross Ref
- Du, X., Kowalski, M., Varde A., de Melo, G. and Taylor, R. 2019. Public opinion matters: mining social media text for environmental management. ACM SIGWEB (Autumn): 5:1--5:15.Google Scholar
- Escrivá-Escrivá, G. 2011. Basic actions to improve energy efficiency in commercial buildings in operation. Energy and Buildings, 43(11), 3106--3111.Google ScholarCross Ref
- Environmental Protection Agency (EPA). 2022. Energy and the Environment. USEPA. Accessed from https://www.epa.gov/energyGoogle Scholar
- Farooqi, A. M. 2017. Comparative Analysis of Green Cloud Computing. International Journal of Advanced Research in Computer Science, 8(2).Google Scholar
- Gandhe, K., Varde, A. and Du, X. 2018. Sentiment Analysis of Twitter Data with Hybrid Learning for Recommender Applications. IEEE UEMCON, 57--63.Google Scholar
- Hasbullah, S.S., and Wan-Chik, R. 2015. Sentiment Analysis of Government Social Media Towards an Automated Content Analysis Using Semantic Role Labeling. 3rd International Conference on Artificial Intelligence and Computer Science (AICS 2015), 12--13.Google Scholar
- Ho, S. S., Chuah, A. S., KIm, N., and Tandoc Jr, E. C. 2022. Fake news, real risks: How online discussion and sources of fact-check influence public risk perceptions toward nuclear energy. Risk Analysis, 2022 Nov;42(11):2569--2583. PMID: 35759611. Google ScholarCross Ref
- Hong, T., Chen, Y., Lee, S. H., and Piette, M. A. 2016. CityBES: A web-based platform to support city-scale building energy efficiency. Urban Computing.Google Scholar
- IEA. 2022. Energy Efficiency 2022. IEA, Paris. https://www.iea.org/reports/energy-efficiency-2022, License: CC by 4.0.Google Scholar
- IJERT. 2021. Survey on energy consumption in cloud computing. IJERT - International Journal of Engineering Research & Technology. Accessed via - https://www.ijert.org/survey-on-energy-consumption-in-cloud-computingGoogle Scholar
- Jain, A., Mishra, M., Peddoju, S.K. and Jain, N. 2013. Energy Efficient Computing-Green Cloud Computing. IEEE. 978-1-4673-6150-7/13/$31.00.Google Scholar
- Jena, T., Mohanty, J.R. and Sahoo, R. 2015. "Paradigm shift to green cloud computing", J. Theor. Appl.Inform. Technol., 77(3), 1--10.Google Scholar
- Kaur, P., and Edalati, M. 2022. Sentiment analysis on electricity twitter posts. arXiv preprint arXiv:2206.05042.Google Scholar
- Kim, S. Y., Ganesan, K., Dickens, P. and Panda, S. 2021. Public sentiment toward solar energy-opinion mining of twitter using a transformer-based language model. Sustainability, 13(5), 2673.Google ScholarCross Ref
- Kommu, A., Patel, S., Derosa, S., Wang, J. and Varde A. S. 2022. HiSAT: Hierarchical Framework for Sentiment Analysis on Twitter Data, IntelliSys (Intelligent Systems Conference), Springer, 376--392.Google Scholar
- Krioukov, A., Dawson-Haggerty, S., Lee, L., Rehmane, O., and Culler, D. 2011. A living laboratory study in personalized automated lighting controls. In Proceedings of the third ACM workshop on embedded sensing systems for energy-efficiency in buildings, 1--6.Google Scholar
- Krotofil, M., Larsen, J., and Gollman, D. 2015. The process matters: Ensuring data veracity in cyber-physical systems. In Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security (pp. 133--144).Google Scholar
- Lee, Y. M, An, L., Liu, F., Horesh, R., Chae, Y. T., and Zhang, R. 2013. "Applying science and mathematics to big data for smarter buildings," Annals of the New York Academy of Sciences, 1295(1), 18--25.Google ScholarCross Ref
- Li, M., and Du, W. 2021. Can Internet development improve the energy efficiency of firms: Empirical evidence from China. Energy, 237, 121590.Google ScholarCross Ref
- Li, J., Guo, Y., Zhang, X., and Zhanbao, F. 2021. Using Hybrid Machine Learning Methods to Predict and Improve the Energy Consumption Efficiency in Oil and Gas Fields. Mobile Information Systems, vol. 2021, Article ID 5729630. Google ScholarDigital Library
- Loureiro, M. L., and Alló, M. 2020. Sensing climate change and energy issues: Sentiment and emotion analysis with social media in the UK and Spain. Energy Policy, 143, 111490.Google ScholarCross Ref
- Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv, arXiv:1907.11692.Google Scholar
- Malekpour Koupaei, D., & Cetin, K. 2021. Smart thermostats in rental housing units: Perspectives from landlords and tenants. Journal of Architectural Engineering, 27(4), 04021042.Google ScholarCross Ref
- Nimbalkar, S., Guo, W., Petri, C., Cresko, J., Graziano, D.J., Morrow, W.R., III, and Wenning, T. Smart Manufacturing Technologies and Data Analytics for Improving Energy Efficiency in Industrial Energy Systems. 2017. In Proceedings of the American Council for Energy Efficient Economy, Denver, CO, USA, 15--18.Google Scholar
- Pang, B. and Lee, L. 2008. Opinion Mining and Sentiment Analysis. Foundations and Trends®. Information Retrieval, 2(1--21), 135. Google ScholarDigital Library
- Pawlish, M., Varde, A. and Robila, S. 2015. The Greening of Data Centers with Cloud Technology. International Journal of Cloud Applications and Computing. International Journal of Cloud Applications and Computing. 5(4), 1--23.Google ScholarDigital Library
- Pawlish, M., and Varde, A. 2010. Free Cooling: A Paradigm Shift in Data Centers, IEEE International Conference on Information and Automation for Sustainability, 347--352.Google Scholar
- Pawlish, M., Varde, A. Robila, S. and Ranganathan, A. 2014. A call for energy efficiency in data centers. ACM SIGMOD Record, 43(1): 45--51.Google ScholarDigital Library
- Prasad, A., Varde, A. Gottimukkala, R., Alo, C. and Lal. P. 2021. Analyzing Land Use Change and Climate Data to Forecast Energy Demand for a Smart Environment, IEEE International Conference on Renewable and Sustainable Energy, IRSEC, pp. 1--6.Google ScholarCross Ref
- Puri, M., Varde, A., Du, X. and de Melo, G. 2018. Smart Governance Through Opinion Mining of Public Reactions on Ordinances. IEEE International Conference on Tools with Artificial Intelligence, ICTAI, Volos, Greece, pp. 838--845.Google Scholar
- Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L., and Xu, X. 2020. Smart factory in Industry 4.0. Syst. Res. Behav. Sci., 37, 607--617.Google ScholarCross Ref
- Singh, A., Yadav, J., Shrestha, S., and Varde, A. 2023. Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index, AAAI Conference on Artificial Intelligence (Workshops Program), Washington, DC, Volume: AI for Social Good, arXiv:2303.08286Google Scholar
- Tan, Y.S., Ng, Y.T., and Low, J.S.C. 2017. Internet-of-Things Enabled Real-time Monitoring of Energy Efficiency on Manufacturing Shop Floors. Procedia CIRP, 61, 376--381.Google ScholarCross Ref
- Varde, A. and Liang, J. 2023. Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability. AAAI Conference on Artificial Intelligence (Bridge Program), Washington, DC. Volume: AI for Materials Science (AIMAT), arXiv:2303.08291Google Scholar
- Varde, A., Liang, J., Sisson, R., and Yang, Z. 2022. Ishikawa, JESS, and Visual Analytics for Engineering. IEEE International Conference on Big Data, Osaka, Japan, pp. 6824--6826.Google Scholar
- Wang, Z., Liu, J., Zhang, Y., Yuan, H., Zhang, R., and Srinivasan, R. S. 2021. Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles. Renewable and Sustainable Energy Reviews, 143, 110929.Google ScholarCross Ref
- Wu, H., Hao, Y., Ren, S., Yang, X., & Xie, G. 2021. Does internet development improve green total factor energy efficiency? Evidence from China. Energy Policy, 153, 112247.Google ScholarCross Ref
- Wu, Z., Yang, K., Yang, J., Cao, Y., & Gan, Y. 2019. Energy-efficiency-oriented scheduling in smart manufacturing. Journal of Ambient Intelligence and Humanized Computing, 10, 969--978.Google ScholarCross Ref
- Yang, X., Liu, S., Zou, Y., Ji, W., Zhang, Q., Ahmed, A., Han, X., Shen, Y., & Zhang, S. 2022. Energy-saving potential prediction models for large-scale building: A state-of-the-art review. Renewable and Sustainable Energy Reviews, 156. Google ScholarCross Ref
Index Terms
- Roles of the Web in Commercial Energy Efficiency: IoT, Cloud Computing, and Opinion Mining
Index terms have been assigned to the content through auto-classification.
Recommendations
Energy-Efficiency in Cloud Computing Environments: Towards Energy Savings without Performance Degradation
Due to all the pollutants generated during its production and the steady increases in its rates, energy consumption is causing serious environmental and economic problems. In this context, the growing use and adoption of ICTs is being highlighted not ...
Green Cloud? An Empirical Analysis of Cloud Computing and Energy Efficiency
The rapid, widespread adoption of cloud computing over the last decade has sparked debates on its environmental impacts. Given that cloud computing alters the dynamics of energy consumption between service providers and users, a complete understanding of ...
Energy efficient hybrid policy in green cloud computing
ICGCIOT '15: Proceedings of the 2015 International Conference on Green Computing and Internet of Things (ICGCIoT)As we know Energy is crucial research in today's world. Due to emerging trends in technology, consumption of energy is growing independently but energy production capabilities are not growing as fast so, efforts to produce more energy is proving hazards ...
Comments