Conference Agenda

School buildings in Manila are ill equipped to deal with the high demand for student places. This has manifested in overly dense classrooms, which in combination with standardised geometries has led to poor thermal and daylighting conditions. This research contextualises passive design strategies from literature and built precedents then applies these approaches to a theoretical classroom. The result is a design proposal that improves indoor comfort through simple interventions in geometry, ventilation, and materiality.

This paper presents the application of Artificial Neural Network (ANN) algorithms to develop forecast models to predict future energy consumption, outdoor weather and indoor microclimatic conditions in a historical art gallery. Each of these prediction models were implemented on two separate cases of sampling frequencies – daily and hourly sampling; providing a case of day-ahead and a case of hour-ahead predictions, respectively. The ANN models were trained with historical real-data obtained from the various sources, such as building sensors, building management information, and MetOffice. Excellent accuracy in the prediction results were observed through the statistical platform of coefficient of correlation (R) between the real-data and the ANN-predicted counterpart. It was observed that the prediction models for hour-ahead forecasting performed stronger compared to the same for day-ahead forecasting for all the cases of outdoor weather parameters, indoor microclimatic parameters, and NGS energy consumption parameters. The study further reinstates that the ANN-based forecast models can prove to be an ideal platform to investigate various optimisation strategies of the building operation in future, especially in the case of restrictive traditional building types where any retrofit solution needs a strong scientific backing before practical implementation.

As a response to the need to find urban solutions to the energy and food dependency in Singapore and to reduce the overall carbon footprint the concept of productive facades is proposed for residential buildings. Departing from the premise that buildings and the urban environment should not solely be the recipient but also the producer of energy, food and water; eight façade design arrangements have been optimised and built at the Tropical Technologies Lab at the National University of Singapore. All proposed facades, with and without balconies, integrates photovoltaic (PV) panels with farming systems as a way to partially supply energy and vegetables to the residents. In addition, the impact of the façade arrangement on indoor thermal and visual performance is also taken into account. The objective of the paper is to present the final design of the productive façade prototypes and the measurement strategy corresponding to the first three months from August till October 2018 in terms of PV electricity generation, vegetable growth and indoor thermal and visual conditions. A comparison with simulation results is expected to be made for four façade systems.

Most methods to analyse and understand the residential energy use features rely on invasive measurements, such as energy monitoring systems, which eventually affects the reliability of pattern classifications. This paper, thus, adopts a non-invasive method using unsupervised data mining algorithms to analyse hourly energy consumption data in order to learn the occupant’s lifestyle and energy consumption behavioral patterns. The study analyses hourly energy use of 298 households in Texas in 2015, using an online open source data set - Pecan Street Dataport. This study scientifically identified household’s energy use features and associated behavioural patterns through a multi scale observation of the clusters. As the contribution, this study takes the house age and size into account as these variables may significantly affect building energy use patterns. Second, it takes dissimilarity measures into account by using TSclust R package for clustering time series. And third, introduces a method of multiscale observation of clusters in order to interpret the lifestyle patterns. Finally, the results demonstrated how data mining techniques might be utilized to help investigating energy use data from the behavioural perspective.