TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

Morcillo-Jiménez, Roberto; Gutiérrez-Batista, Karel; Gómez‐Romero, Juan

doi:10.3390/en16041581

Cited by 2 publications

(2 citation statements)

References 72 publications

(79 reference statements)

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“…This section shows the results of the experiments after data preprocessing and model training and validation with the algorithms mentioned above: decision trees, XGBoost, and neural networks (MLPs, CNNs, RNNs and Seq2Seq). The implementation of the preprocessing and the prediction algorithms was developed with TSxtend [23], our open source library for batch analysis of sensor data.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Deep learning for prediction of energy consumption: an applied use case in an office building

Morcillo-Jimenez,

Mesa,

Gómez-Romero

et al. 2024

Appl Intell

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Non-residential buildings are responsible for more than a third of global energy consumption. Estimating building energy consumption is the first step towards identifying inefficiencies and optimizing energy management policies. This paper presents a study of Deep Learning techniques for time series analysis applied to building energy prediction with real environments. We collected multisource sensor data from an actual office building under normal operating conditions, pre-processed them, and performed a comprehensive evaluation of the accuracy of feed-forward and recurrent neural networks to predict energy consumption. The results show that memory-based architectures (LSTMs) perform better than stateless ones (MLPs) even without data aggregation (CNNs), although the lack of ample usable data in this type of problem avoids making the most of recent techniques such as sequence-to-sequence (Seq2Seq). Graphical abstract

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Section: Experiments and Resultsmentioning

confidence: 99%

Deep learning for prediction of energy consumption: an applied use case in an office building

Morcillo-Jimenez,

Mesa,

Gómez-Romero

et al. 2024

Appl Intell

Self Cite

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“…Xel [35], auto-sklearn (https://github.com/automl/auto-sklearn, accessed on 5 January 2024), TSxtend [36], K-NIME (https://www.knime.com/, accessed on 5 January 2024), and MLFLOW enhancement [37]. All these open-source tools, prototypes, and pipelines include basic functionalities (e.g., data pre-processing, feature selection, feature extraction, model selection, etc.)…”

Section: Implementation Of Dc-ai Approachmentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

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TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

Cited by 2 publications

References 72 publications

Deep learning for prediction of energy consumption: an applied use case in an office building

Deep learning for prediction of energy consumption: an applied use case in an office building

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

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