Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Movahedi, Ali; Derrible, Sybil

doi:10.31224/osf.io/ahn3e

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…In 2017, Lundberg and Lee developed a Python package that could calculate the SHAP for various technologies, including LightGBM, XGBoost, Gboost, CatBoost Scikit-learn, and tree models [22]. For the interpretation of AI models that are difficult to interpret, many researchers have begun using SHAP [23]. Wang et al proposed a framework to provide regional and global explanations for IDS judgments [24].…”

Section: Xaimentioning

confidence: 99%

Automatic False Alarm Detection Based on XAI and Reliability Analysis

Lee

2022

Applied Sciences

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Many studies attempt to apply artificial intelligence (AI) to cyber security to effectively cope with the increasing number of cyber threats. However, there is a black box problem such that it is difficult to understand the basis for AI prediction. False alarms for malware or cyberattacks can cause serious side effects. Due to this limitation, all AI predictions must be confirmed by an expert, which is a considerable obstacle to AI expansion. Compared to the increasing number of cyberattack alerts, the number of alerts that can be analyzed by experts is limited. This paper provides explainability through an interpretation of AI prediction results and a reliability analysis of AI predictions based on explainable artificial intelligence (XAI). In addition, we propose a method for screening high-quality data that can efficiently detect false predictions based on reliability indicators. Through this, even a small security team can quickly respond to false predictions. To validate the proposed method, experiments were conducted using the IDS dataset and the malware dataset. AI errors were detected better than they could be by the existing AI models, with about 262% in the IDS dataset and 127% in the malware dataset from the top 10% of analysis targets. Therefore, the ability to respond to cyberattacks can be improved using the proposed method.

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Section: Xaimentioning

confidence: 99%

Automatic False Alarm Detection Based on XAI and Reliability Analysis

Lee

2022

Applied Sciences

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“…In 2017, Lundberg and Lee developed a Python package that can calculate the SHAP for various technologies including LightGBM, XGBoost, GBoost, CatBoost Scikit-learn, and tree models [23]. For the interpretation of AI models that are difficult to interpret, many researchers have begun using SHAP [24]. SHAP uses the SHAP value as a basis for explanation.…”

Section: ) Shapley Additive Explanations (Shap) Figure 1 Evaluation Explanation Effectiveness [18]mentioning

confidence: 99%

Cost-Effective Valuable Data Detection Based on the Reliability of Artificial Intelligence

Kim

Lee

et al. 2021

IEEE Access

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Many previous studies have investigated applying artificial intelligence (AI) to cyber security. Despite considerable performance advantages, AI for cyber security requires final confirmation by an analyst, e.g. malware misdetection can cause significant adverse side effects. Thus, all AI predictions must be checked by a human analyst, which poses a major obstacle to AI expansion. This paper proposes a reliability indicator for AI prediction using explainable artificial intelligence and statistical analysis techniques. This will enable analysts with limited daily workload to focus upon valuable data, and quickly verify AI predictions. Analysts generally make decisions based on several features that they know exactly what they mean, rather than all available features. Since the proposed reliability indicator is calculated using features meaningful to analysts, it can be easily understood and hence speed final decisions. To verify the performance of the proposed method, an experiment was conducted using the IDS dataset and the malware dataset. The AI error was detected better than the existing AI model at about 114% in IDS and 95% in malware. Thus, cyberattack response could be greatly improved by adopting the proposed method.

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“…Exploring the interactions between water consumption and energy use 2016) conducted a review of the water-energy nexus to reveal the complex links between water and electricity generation. Movahedi and Derrible (2020) studied the interrelationships between water, electricity, and gas consumption in large-scale buildings in New York City. Figure 7.6 shows a hybrid Sankey diagram depicting interconnected water and energy flows in the United States in 2011, developed by the US Department of Energy (Bauer et al 2014).…”

Section: Urban Metabolism Applications Challenges and Opportunitiesmentioning

confidence: 99%

Urban Metabolism

Derrible

Cheah

Arora

et al. 2021

The Urban Book Series

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Urban metabolism (UM) is fundamentally an accounting framework whose goal is to quantify the inflows, outflows, and accumulation of resources (such as materials and energy) in a city. The main goal of this chapter is to offer an introduction to UM. First, a brief history of UM is provided. Three different methods to perform an UM are then introduced: the first method takes a bottom-up approach by collecting/estimating individual flows; the second method takes a top-down approach by using nation-wide input–output data; and the third method takes a hybrid approach. Subsequently, to illustrate the process of applying UM, a practical case study is offered using the city-state of Singapore as an exemplar. Finally, current and future opportunities and challenges of UM are discussed. Overall, by the early twenty-first century, the development and application of UM have been relatively slow, but this might change as more and better data sources become available and as the world strives to become more sustainable and resilient.

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Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Cited by 9 publications

References 39 publications

Automatic False Alarm Detection Based on XAI and Reliability Analysis

Automatic False Alarm Detection Based on XAI and Reliability Analysis

Cost-Effective Valuable Data Detection Based on the Reliability of Artificial Intelligence

Urban Metabolism

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