• Energy Research

Nowadays the ever-increasing energy consumption in buildings has caused supply shortages and adverse environmental impacts. The accurate prediction of energy consumption in smart buildings may help to monitor and control energy usage. As energy consumption is inevitably affected by exogenous factors such as temperature and wind speed, it is fundamentally important to select the informative channels of the factors, to extract the valuable features from the selected channels applied to the optimal-configured model, to improve prediction accuracy. However, existing work considers these parts in an almost disjoint way and lacks a model taking them into account, which may decrease prediction performance. Motivated by this challenge, an end-to-end prediction framework, called evolutionary model construction (EMC), is proposed to focus on performing these parts jointly. To implement EMC, a two-step evolutionary algorithm (EA) is designed, where one EA is firstly used to focus on exploiting the informative channels, while a new algorithm is proposed to concentrate on selecting the suitable feature extraction methods and respective time window sizes applied to the selected channels, and selecting the parameters in the predictor. The implementation of EMC chooses neural network with random weights as the predictor due to its highly recognized efficacy. We evaluate EMC in comparison with the existing approaches on a real-world electricity consumption dataset with various auxiliary factors. The superiority of EMC is further proved by analyzing and discussing the result according to the days in 1 week, time stamps in 1 day and month information on test samples.

The developments in sensing modalities and computing platforms enable many new sensing technologies and data sources for monitoring occupant presence and actions. The wealth of data opens new opportunities for extracting knowledge through data-driven modeling of occupant presence and actions. In particular, the many opportunities with machine learning techniques including supervised and unsupervised learning for classification, regression and clustering problems. Utilizing these opportunities creates new models and information relevant for generating new knowledge on multi-aspect environmental exposure, building interfaces, human behaviour, occupant-centric building design and operation. Subtask 2 of the new IEA EBC Annex 79 is addressing these opportunities and is inviting researchers and practitioners to participate. The developed data-driven models can, among others, be applied for example for calculating new schedules or models based on the actual conditions observed in buildings, data-driven analysis of the performance design versus the built, model predictive controls for buildings and fault detection and diagnostics.

Emerging from the challenge to reduce energy consumption in buildings is the need for energy simulation to be used more effectively to support integrated decision making in early design. As a critical response to a Green Star case study, we present DEEPA, a parametric modeling framework that enables architects and engineers to work at the same semantic level to generate shared models for energy simulation. A cloud-based toolkit provides web and data services for parametric design software that automate the process of simulating and tracking design alternatives, by linking building geometry more directly to analysis inputs. Data, semantics, models and simulation results can be shared on the fly. This allows the complex relationships between architecture, building services and energy consumption to be explored in an integrated manner, and decisions to be made collaboratively.

Abstract Traditional occupant behavior modeling has been studied at the building level, and it has become an important factor in the investigation of building energy consumption. However, studies modeling occupant behaviors at the urban scale are still limited. Recent work has revealed that urban big data can enable occupant behavior modeling at the urban scale – however, utilizing the existing data sources and modeling methods in building science to model urban scale occupant behaviors can be quite challenging. Beyond building science, urban scale human behaviors have been studied in several different domains using more advanced modeling methods, including Stochastic Modeling, Neural Networks, Reinforcement Learning, Network Modeling, etc. This paper aims to bridge the gap between data sources and modeling methodologies in building science by borrowing from other domains. Based on a comprehensive review, we 1) identify the modeling challenges of the current approaches in building science, 2) discuss the modeling requirements and data sources both in building science and other domains, 3) review the current modeling methods in building science and other domains, and 4) summarize available performance evaluation metrics for evaluating the modeling methods. Finally, we present future opportunities in building science with enhanced data sources and modeling methods from other domains.

AbstractThis paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

Abstract In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants’ actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.