2019) Use case is a model for the behavior of the actor to the information system to be made (Pritoni et al. A framework is a program structure that makes it easier for programmers to create an application so that it will be easier for programmers to make changes (Utami, Zen, and Rauna 2021) To support research on the application of researchers using the Laravel framework, Laravel is a PHP based web-framework for building high-end web applications using its significant and graceful syntaxes (Kausar Bagwan and Swati Ghule 2019) In laravel there is a routing that bridges between requests from users and controllers (Sinha 2019) that the Laravel framework is preferable for large-scale Web projects that require faster delivery with fewer resources (Laaziri et al. The Indonesian government will continue to maintain that domestic economic performance continues to strengthen even in the midst of various global challenges (Fawzi and Husna 2021) In the development of website applications, there is a framework that is used as the basic structure of program code. The compatibility of EFOnt with existing ontologies and the outlook of EFOnt's role in the building energy data tool ecosystem are discussed. We demonstrate potential use cases of EFOnt via two examples: (1) energy flexibility analytics with measured data from a residential smart thermostat dataset and a commercial building, and (2) modeling and simulation to evaluate energy flexibility of buildings. EFOnt aims to serve as a standardized tool for knowledge co-development and streamlining energy flexibility related applications. This paper presents a semantic ontology-EFOnt (Energy Flexibility Ontology)-that extends existing terminologies, ontologies, and schemas for building energy flexibility applications. Although energy flexibility has received growing attention from industry and the research community, there remains a lack of common ground for energy flexibility terminologies, characterization, and quantification methods. Traditional demand-side management technologies, advanced building controls, and emerging distributed energy resources (including electric vehicle, energy storage, and on-site power generation) enable the transition of the building stock to grid-interactive efficient buildings (GEBs) that operate efficiently to meet service needs and are responsive to grid pricing or carbon signals to achieve energy and carbon neutrality. We perform a preliminary analysis into the use of transducer-based language models to parse and normalise building point metadata.Įnergy flexibility of buildings can be an essential resource for a sustainable and reliable power grid with the growing variable renewable energy shares and the trend to electrify and decarbonize buildings. Finite State Transducers can model sequence-to-sequence tasks where the input and output sequences are different lengths, and they can be combined with language models to ensure a valid output sequence is generated. It is also difficult to apply standard techniques such as tokenisation since this commonly results in multiple output tags being associated with a single input token, something traditional sequence labelling models do not allow. Conventional machine learning techniques are inefficient since they need to learn many different forms for the same word, and large amounts of data must be used to train these models. The vocabulary used to describe building metadata appears small compared to general natural languages, but each term has multiple commonly used abbreviations. Solving the challenges of automatic machine translation of Building Automation System text metadata is a crucial first step in efficiently deploying smart building applications.
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