DeepECO: Applying Deep Learning for Occupancy Detection from Energy Consumption Data

Pal, Neelanjana; Ghosh, Purboday; Karsai, Gábor

doi:10.1109/icmla.2019.00311

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…A large value of φ j presents a strong positive impact of a feature on predictions. Depending on the model type, SHAP provides various explainers [76]; some of the commonly used explainers are as follows: (a) TreeExplainer can be used to explain treebased classifiers such as XGBoost, random forest, etc. (b) KernelExplainer can be used to explain any function, (c) DeepExplainer only explains deep neural networks, and (d) GradientExplainer can also be used to explain neural networks frameworks, such as TensorFlow, Keras, and Pytorch [77].…”

Section: Shapley Additive Explanation (Shap)mentioning

confidence: 99%

Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

et al. 2021

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Today, deep learning approaches are widely used to build Intrusion Detection Systems for securing IoT environments. However, the models’ hidden and complex nature raises various concerns, such as trusting the model output and understanding why the model made certain decisions. Researchers generally publish their proposed model’s settings and performance results based on a specific dataset and a classification model but do not report the proposed model’s output and findings. Similarly, many researchers suggest an IDS solution by focusing only on a single benchmark dataset and classifier. Such solutions are prone to generating inaccurate and biased results. This paper overcomes these limitations in previous work by analyzing various benchmark datasets and various individual and hybrid deep learning classifiers towards finding the best IDS solution for IoT that is efficient, lightweight, and comprehensive in detecting network anomalies. We also showed the model’s localized predictions and analyzed the top contributing features impacting the global performance of deep learning models. This paper aims to extract the aggregate knowledge from various datasets and classifiers and analyze the commonalities to avoid any possible bias in results and increase the trust and transparency of deep learning models. We believe this paper’s findings will help future researchers build a comprehensive IDS based on well-performing classifiers and utilize the aggregated knowledge and the minimum set of significantly contributing features.

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Section: Shapley Additive Explanation (Shap)mentioning

confidence: 99%

Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

et al. 2021

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AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

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In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

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Smart Meter Data Analytics for Occupancy Detection of Buildings with Renewable Energy Generation

Allik

Muiste

Pihlap

2020

2020 9th International Conference on Renewable Energy Research and Application (ICRERA)

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DeepECO: Applying Deep Learning for Occupancy Detection from Energy Consumption Data

Cited by 4 publications

References 14 publications

Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

Smart Meter Data Analytics for Occupancy Detection of Buildings with Renewable Energy Generation

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