Active Learning Strategy for High Fidelity Short-Term Data-Driven Building Energy Forecasting

2021 ◽  
pp. 111026
Author(s):  
Liang Zhang ◽  
Jin Wen
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Building Energy ◽  
Data Driven ◽  
High Fidelity ◽  
Short Term ◽  
Energy Forecasting ◽  
Active Learning Strategy ◽  
Term Data

Systematic Feature Selection Process Applied in Short-Term Data-Driven Building Energy Forecasting Models: A Case Study of a Campus Building

2017 ◽  
Author(s):  
Liang Zhang ◽  
Jin Wen ◽  
Yimin Chen
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Building Energy ◽  
Data Driven ◽  
Forecasting Model ◽  
Energy Forecasting ◽  
Systematic Feature ◽  
Selection Framework ◽  
Term Data

An accurate building energy forecasting model is a key component for real-time and advanced control of building energy system and building-to-grid integration. With the fast deployment and advancement of building automation systems, data are collected by hundreds and sometimes thousands of sensors every few minutes in buildings, which provide great potential for data-driven building energy forecasting. To develop building energy forecasting models from a large number of potential inputs, feature selection is a critical procedure to ensure model accuracy and computation efficiency. Though the theory of feature selection is well developed in statistics and machine learning fields, it is not well studied in the application of building energy modeling. In this paper, a feature selection framework proposed in an earlier study is examined using a real campus building in Philadelphia. This feature selection framework combines domain knowledge and statistical methods and is developed for short-term data-driven building energy forecasting. In this case study, the feasibilities of using this feature selection framework in developing whole building energy forecasting model and chiller energy forecasting model are studied. Results show that, for both whole building and chiller energy forecasting applications, the model with systematic feature selection process presents better performance (in terms of cross validation error of forecasted output) than other models including that with conventional inputs and that uses only single feature selection technique.

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals Enhancing generic capabilities and metacognitive awareness of first-year nursing students using active learning strategy

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Carmen Wing Han Chan ◽  
Fiona Wing Ki Tang ◽  
Ka Ming Chow ◽  
Cho Lee Wong
Keyword(s):  
Nursing Education ◽  
Nursing Students ◽  
Active Learning ◽  
Flipped Classroom ◽  
Learning Strategy ◽  
Interactive Learning ◽  
Metacognitive Awareness ◽  
University Education ◽  
Strong Impact ◽  
Active Learning Strategy

Abstract Background Developing students’ generic capabilities is a major goal of university education as it can help to equip students with life-long learning skills and promote holistic personal development. However, traditional didactic teaching has not been very successful in achieving this aim. Kember and Leung’s Teaching and Learning Model suggests an interactive learning environment has a strong impact on developing students’ generic capabilities. Metacognitive awareness is also known to be related to generic capability development. This study aimed to assess changes on the development of generic capabilities and metacognitive awareness after the introduction of active learning strategy among nursing students. Methods This study adopted a quasi-experimental single group, matched pre- and posttest design. It was conducted in a school of nursing at a university in Hong Kong. Active learning approaches included the flipped classroom (an emphasis on pre-reading) and enhanced lectures (the breaking down of a long lecture into several mini-lectures and supplemented by interactive learning activities) were introduced in a foundational nursing course. The Capabilities Subscale of the Student Engagement Questionnaire and the Metacognitive Awareness Inventory were administered to two hundred students at the start (T0) and at the end of the course (T1). A paired t-test was performed to examine the changes in general capabilities and metacognitive awareness between T0 and T1. Results A total of 139 paired pre- and post-study responses (69.5 %) were received. Significant improvements were observed in the critical thinking (p < 0.001), creative thinking (p = 0.03), problem-solving (p < 0.001) and communication skills (p = 0.04) with the implementation of active learning. Significant changes were also observed in knowledge of cognition (p < 0.001) and regulation of cognition (p < 0.001) in the metacognitive awareness scales. Conclusions Active learning is a novel and effective teaching approach that can be applied in the nursing education field. It has great potential to enhance students’ development of generic capabilities and metacognitive awareness.

STORYTELLING AND STORYACTING AS AN ACTIVE LEARNING STRATEGY

1999 ◽  
Vol 20 (3) ◽  
pp. 347-352 ◽  
Author(s):  
Karen Cachevki Williams ◽  
Margaret Cooney ◽  
Jane Nelson
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Active Learning Strategy

scholarly journals LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition

2022 ◽  
Author(s):  
Mingyi Liu ◽  
Zhiying Tu ◽  
Tong Zhang ◽  
Tonghua Su ◽  
Xiaofei Xu ◽  
...  
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Active Learning Strategy

scholarly journals PENGGUNAAN STRATEGI PEMBELAJARAN AKTIF EVERYONE IS A TEACHER HERE (ETH) TERHADAP KEMAMPUAN PEMAHAMAN KONSEP MATEMATIKA SISWA KELAS VII

2018 ◽  
Vol 4 (1) ◽  
pp. 95-100
Author(s):  
Sri Yunita Ningsih ◽  
Gustimalasari Gustimalasari
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Control Group ◽  
Control Group Design ◽  
Concept Understanding ◽  
Group Design ◽  
Quasi Experimental ◽  
Active Learning Strategy ◽  
And Control ◽  
Better Than

Abstract. This research has been made to know skill of student’s concept by using active learning strategy everyone is teacher here (ETH). Beside that this study aims to measure student’s concept understanding with statistical test between Experimental Class (Active Learning Strategy Everyone Is Teacher Here) and control class (Conventional Learning ). The population was seventh grade of SMPN 3 Lirik consist 94 students in three classes. Sample was took randomly, experiment class ( VII.2 ) and control class ( VII.I ) This research was experiment, the form of this research was Quasi Experimental Design with randomized subject posttest only control group design. based on statistic data processing has been retrieved - t hitung -3,159 smaller than - t table was -2,000 and based on t test has been retrieved -thitung < -t table so Ho rejected and Ha received. So that the writer conclude that skill of math student’s concept understanding by using active learning Strategy Everyone Is Teacher Here (ETH) is better than conventional concept understanding.Keywords: Everyone Is A Teacher Here, Concept Understanding

scholarly journals Batch Decorrelation for Active Metric Learning

2020 ◽  
Author(s):  
Priyadarshini Kumari ◽  
Ritesh Goru ◽  
Siddhartha Chaudhuri ◽  
Subhasis Chaudhuri
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Metric Learning ◽  
Parametric Models ◽  
Distance Metrics ◽  
Learning Approaches ◽  
Fundamental Goal ◽  
Novel Method ◽  
Active Learning Strategy ◽  
Perceptual Metrics

We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$. In contrast to prior work on class-based learning, where the fundamental goal is classification and any implicit or explicit metric is binary, we focus on perceptual metrics that express the degree of (dis)similarity between objects. We find that standard active learning approaches degrade when annotations are requested for batches of triplets at a time: our studies suggest that correlation among triplets is responsible. In this work, we propose a novel method to decorrelate batches of triplets, that jointly balances informativeness and diversity while decoupling the choice of heuristic for each criterion. Experiments indicate our method is general, adaptable, and outperforms the state-of-the-art.

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