Hafiz Areeb Asad

简介

We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, for which they require more accurate energy intake predictions. Our study is based on a data set covering over 3 years, for which we simulate online machine learning algorithms with different amounts of training data and input features. Our results show that relatively simple machine learning methods can outperform a persistent predictor considerably, and we observed a reduction of errors of up to 56%. When devices obtain a significant amount of sunlight, adding the weather forecast improves the prediction accuracy. We discuss prediction features, the amount of trainin

代表成果

• Scientific articles and book chapters
• Asad, Hafiz Areeb; Kraemer, Frank Alexander; Bach, Kerstin & Renner, Bernd-Christian (2025). UtiliGEM: Energy Management Guided by Learned Application Utility. In Peltonen, Ella & Hyrynsalmi, Sami (Ed.), IoT '24: Proceedings of the 14th International Conference on the Internet of Things. Association for Computing Machinery (ACM). ISSN 9798400712852. p. 47–55. doi: 10.1145/3703790.3703796. Full text in Research Archive
• Krämer, Frank Alexander; Asad, Hafiz Areeb; Bach, Kerstin & Renner, Christian (2023). Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy. IEEE Access. 11, p. 38417–38425. doi: 10.1109/ACCESS.2023.3267810. Full text in Research Archive Show summary We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, for which they require more accurate energy intake predictions. Our study is based on a data set covering over 3 years, for which we simulate online machine learning algorithms with different amounts of training data and input features. Our results show that relatively simple machine learning methods can outperform a persistent predictor considerably, and we observed a reduction of errors of up to 56%. When devices obtain a significant amount of sunlight, adding the weather forecast improves the prediction accuracy. We discuss prediction features, the amount of training data and analyze the sources of errors to understand the potential of indoor photovoltaic energy harvesting predictions.
• Veiga, Tiago Santos; Asad, Hafiz Areeb; Kraemer, Frank Alexander & Bach, Kerstin (2023). Container-Based IoT Architectures: Use Case for Visual Person Counting. In Galimullin, Rustam & Touileb, Samia (Ed.), Proceedings of the 5th Symposium of the Norwegian AI Society (NAIS 2023). NAIS Norwegian Artificial Intelligence Society. Full text in Research Archive Show summary This paper studies the deployment process for a use case of visual person counting from cameras located in outdoor areas and shows how a containerized solution fulfills the particular requirements for the use case, illustrating how the design of the modular architecture, data pipelines, and exposed services contribute to enhancing adaptive behavior through learning based on the context of the environment.
• Asad, Hafiz Areeb; Kraemer, Frank Alexander; Bach, Kerstin; Renner, Christian & Veiga, Tiago Santos (2022). Learning attention models for resource-constrained, self-adaptive visual sensing applications. In Li, Peng; Heo, Junyoung & Cerny, Tomas (Ed.), RACS '22: Proceedings of the Conference on Research in Adaptive and Convergent Systems. Association for Computing Machinery (ACM). ISSN 9781450393980. p. 165–171. doi: 10.1145/3538641.3561505. Full text in Research Archive
• Veiga, Tiago Santos; Asad, Hafiz Areeb; Kræmer, Frank Alexander & Bach, Kerstin (2022). Towards containerized, reuse-oriented AI deployment platforms for cognitive IoT applications. Future Generation Computer Systems. ISSN 0167-739X. 142, p. 4–13. doi: 10.1016/j.future.2022.12.029. Full text in Research Archive Show summary IoT applications with their resource-constrained sensor devices can benefit from adjusting their operations to the phenomena they sense and the environments they operate in, leading to the paradigm of self-adaptive, autonomous, or cognitive IoT. On the other side, current AI deployment platforms focus on the provision and reuse of machine learning models through containers that can be wired together to build new applications. The challenge is that composition mechanisms of the AI platforms, albeit effective due to their simplicity, are in fact too simplistic to support cognitive IoT applications, in which sensor devices also benefit from the machine learning results. Our objective is to perform a gap analysis between the requirements of cognitive IoT applications on the one side and the current functionalities of AI deployment platforms on the other side. In this work, we provide an overview of the paradigms in AI deployment platforms and the requirements of cognitive IoT applications. We study a use case for person counting in a skiing area through camera sensors, and how this use case benefits from letting the IoT sensors have access to operational knowledge in the form of visual attention models. We describe the implementation of the IoT application using an AI deployment platform, analyze its shortcomings, and necessary workarounds. From the use case, we identify and generalize five gaps that limit the usage of deployment platforms: the transparent management of multiple instances of components, a more seamless integration with IoT devices, explicit definition of data flow triggers, and the availability of templates for cognitive IoT architectures and reuse below the top-level.
• Veiga, Tiago; Asad, Hafiz Areeb; Kraemer, Frank Alexander & Bach, Kerstin (2023). Container-Based IoT Architectures: Use Case for Visual Person Counting. Full text in Research Archive