180 research outputs found
Leveraging Machine Learning to Forecast Neighborhood Energy Use in Early Design Stages: A Preliminary Application
Get PDFThe need for energy efficiency in neighborhood-scale architectural design is driven by environmental imperatives and escalating energy costs. This study identifies three key phases in a design process framework where machine learning can be applied to optimize energy consumption in early design stages. The overall framework integrates machine learning tools into the design workflow, enhancing design exploration from concept level and enabling targeted energy assessments. This paper focuses on the first phase (Phase 1) of the framework, which employs machine learning for building energy forecasting using only the few inputs available in a business-as-usual early-stage design workflow. The CatBoost model was selected for its high accuracy in predicting energy consumption using minimal input data. A preliminary application to a case study in New York City showed high predictive accuracy while reducing the input needed, with R2 scores of 0.88 for both cross-validation and test datasets. Shapely additive explanation analysis validated the selection of key influencing parameters such as building area, principal building activity, and climate zones. The test demonstrated discrepancies between the test data-driven model and a physics-based energy model values ranging from −8.69% to 11.04%, which can be considered an acceptable result in early-stage design. The remaining two phases, though outside the scope of this study, are introduced at a conceptual level to provide an overview of the full framework. Phase 2 will analyze building shape and elevation, assessing the total energy use intensity, while Phase 3 will apply district-level energy optimization across interconnected buildings. The findings from Phase 1 underscore the potential of machine learning to integrate energy efficiency considerations into neighborhood-scale design from the earliest stages, providing reliable predictions that can inform sustainable design
Integrated Workflow Development for Data-Driven Neighborhood-Scale Building Performance Simulation
Get PDFAs urbanization intensifies, cities are key contributors to energy consumption and carbon emissions, accounting for a significant portion of global energy use and CO2 emissions. This paper introduces a systematic approach to support the development of urban projects with minimized operational carbon footprints through the integration of data-driven building performance simulation (BPS) tools in early-stage design. Emphasizing the necessity for a collaborative effort among designers, policymakers, and other stakeholders, we discuss the evolution of BPS toward incorporating data-driven tools for energy need reduction and informed decision-making. Despite the proliferation of modeling methods and data-related challenges, we present a theoretical workflow, supported by interactions with design firms in the US and European Union (EU) through interviews. This structured approach, demonstrating adaptability and scalability across urban contexts, foregrounds the potential for future data-driven integration in design practices. Grounded in theoretical concepts and preliminary real-world insights, our work emphasizes the transformation of standard activities toward data-driven processes, showcasing the crucial role of practical experience in advancing sustainable, low-carbon urban development
Optimizing Urban Energy Efficiency Through a Machine Learning-Driven Framework: A Case Study in Reykjavik
No full textThis paper explores the potential of digital tools to guide early-stage design towards energy efficiency at the neighborhood scale, addressing escalating environmental concerns and energy costs. This paper tests a novel, data-driven framework that employs machine learning to optimize urban energy consumption by analyzing building typology, morphology, and energy usage patterns. The framework operates through a multi-phase urban design strategy, beginning with evaluating building forms and climate data to provide early insights into energy usage.
Subsequent phases predict and optimize energy use intensity (EUI) across individual buildings, incorporating technical assumptions and seasonal variations to refine predictions and reduce overall energy consumption. Finally, the methodology extends to a comprehensive urban scale, focusing on clusters of buildings. This integrated approach is tested through a case study in the Ártúnshöfði area of Reykjavik, Iceland. The framework’s applicability is evaluated through two phases, replicating a standard workflow. Initially, multiple architectural options and building typologies are evaluated based on building coverage ratio, green coverage ratio, and shape factor.
Following the selection of the most efficient solution, a machine learning algorithm using ensemble techniques like CatBoost Regressor assesses the energy use intensity of nine different solutions. The results show high predictive accuracy, with R2 scores of 0.88 for both cross-validation and test datasets. The model demonstrated reliability, with discrepancies between the test model and a business-as-usual energy model ranging from -9 to +12%. These findings underscore the framework’s potential to integrate energy efficiency into urban planning from the earliest stages, providing accurate and reliable predictions that can inform sustainable design and policymaking. Moreover, the machine learning-based framework avoids the need for
numerous detailed point simulations, streamlining the overall design process
Extracting Dependency Relations for Opinion Mining
No full textIntent mining is a special kind of document analysis whose goal is to assess the attitude of the document author with respect to a given subject. Opinion mining is a kind of intent mining where the attitude is a positive or negative opinion. Techniques based on extracting dependency relations have proven more effective for intent mining than traditional bag-of-word approaches. We propose an approach to opinion mining which uses frequent dependency sub-trees as features for classifying documents and extracting opinions. We developed an efficient multi-language dependency parser to analyze documents and extracting dependency relations which can be used on large scale collections. An opinion retrieval system has been built and is being tested on the TREC 2006 Blog Opinion task
Framing otherness in the US political discourse around the Ukranian-Russian war. The case of pronouns
No full textIl discorso politico spesso denota come il linguaggio venga manipolato per raggiungere un proprio fine. Questo studio indaga la manipolazione della deissi nel discorso politico Statunitense in merito alla guerra Russo-Ucraina. Utilizzando tecniche di linguistica dei corpora, questo articolo analizza l’uso dei pronomi nei discorsi pronunciati al Senato degli Stati Uniti durante il primo anno di conflitto Russo-Ucraino dai rappresentanti dei due maggiori partiti Statunitensi: Partito Democratico e Partito Repubblicano. Attraverso un approccio corpus-assisted, questa ricerca approfondisce gli aspetti qualitativi e quantitativi di come i rappresentanti politici utilizzino la deissi per costruire un’identità e influenzare gli ascoltatori guidandoli verso una particolare prospettiva.In political discourse, language is often manipulated to achieve one’s own political end. The present study aims at investigating the manipulation of person deixis in the United States political discourse around the Ukranian-Russian war. Through the framework of corpus linguistics, the author will analyse the use of pronouns in a corpus of speeches made at the United States Senate during the first year of conflict from the representatives of the two major parties of the United States: Democrat and Republican. The corpus includes the speeches delivered between February 2022 and February 2023 in relation to the Ukranian-Russian war. By means of a corpus-assisted approach, this research will delve into the qualitative and quantitative aspects of how political representatives employ person deixis to construct various facets of identity and sway the audience towards embracing a particular perspective
Generalized Abstracted Mean Values
No full textIn this article, the author introduces the generalized abstracted mean values which extend the concepts of most means with two variables, and researches their basic properties and monotonicities
Blog Mining Through Opinionated Words
No full textIntent mining is a special kind of document analysis whose goal is to assess the attitude of the document author with respect to a given subject.
Opinion mining is a kind of intent mining where the attitude is a positive or negative opinion.
Most systems tackle the problem with a two step approach, an information retrieval followed by a postprocess or filter phase to identify opinionated blogs. We explored a single stage approach to opinion mining, retrieving opinionated documents ranked with a special ranking function which exploits an index enriched with opinion tags. A set of subjective words are used as tags for identifying opinionated sentences. Subjective words are marked as “opinionated” and are used in the retrieval phase to boost the rank of
documents containing them. In indexing the collection, we recovered the relevant content from the blog permalink pages, exploiting HTML metadata about the generator and heuristics to remove irrelevant parts from the body. The index also contains information about the occurrence of opinionated words, extracted from an analysis of WordNet glosses. The experiments compared the
precision of normal queries with respect to queries which included as constraint the
proximity to an opinionated word. The results show a significant improvement in precision for both topic relevance and opinion relevance
Net Zero Energy Homes: An Evaluation of Two Homes in the Northeastern United States
No full textThis paper will discuss two Net Zero Energy homes in the United States. The aim is to discuss the differences and similarities in the construction type, energy use, active and renewable systems of the two homes. While each of the homes is designed to achieve net zero site energy use, the design and systems are very different. Furthermore, the measure that is used to qualify a home as net zero energy does not account for the full scope of work on each home. It is suggested that a new set of metrics be developed to allow for a more robust understanding of net zero energy buildings, one that integrates passive design strategies, occupant health and comfort, and durability. The objective is to facilitate a broader understanding of efficient and sustainable residential design. This understanding is critical to bringing Net Zero Energy Buildings to the public.</jats:p
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