Historic Building Information Modelling: performance assessment for diagnosis-aided information modelling and management

Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction

A comprehensive review on the application of artificial neural networks in building energy analysis

Building energy consumption prediction plays a key role in energy-efficiency decision making. With the advancement in data analytics, a number of machine learning-based building energy consumption prediction models have been developed in recent years. However, existing prediction models do not sufficiently take occupant behavior into account. Towards addressing this gap, this paper presents a machine-learning approach for predicting building energy consumption in an occupant-behavior-sensitive manner. In this approach, a model learns from a large set of energy-use cases that were modelled and simulated in EnergyPlus. The machine-learning prediction model was trained using a large dataset that includes 3-month hourly data for 5760 energy-use cases representing different combinations of building characteristics, outdoor weather conditions, and occupant behaviors. In developing the model, four machine-learning algorithms were tested and compared in terms of their prediction accuracy and computational efficiency: classification and regression trees (CART), ensemble bagging trees (EBT), artificial neural networks (ANN), and deep neural networks (DNN). The simulation results demonstrated the high impact of the variables considered in this study. For example, the highest energy-consuming case consumed over 3432 times more energy than the lowest-consuming case. Occupant behavior made a difference up to over 7 times in energy consumption. The DNN model with four hidden layers achieved 2.97% coefficient of variation (CV). Such high performance shows the potential of the proposed approach. The approach could help better understand the impact of occupant behavior on building energy consumption and identify opportunities for behavioral energy-saving measures.

Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and air-conditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality and comfort. As a result, machine learning-based models and methodologies are progressively being used to forecast occupancy behaviour and routines in buildings, which may subsequently be used to aid in the design and operation of building systems. The present work reviews recent studies employing machine learning methods to predict occupancy behaviour and patterns, with a special focus on its related applications and benefits to building systems, improving energy efficiency, indoor air quality and thermal comfort. The review provides insight into the workflow of a machine learning-based occupancy prediction model, including data collection, prediction, and validation. An organised evaluation of the applicability or suitability of the different data collection methods, machine learning algorithms, and validation methods was carried out. 

/pdf/a-review-on-occupancy-prediction-through-machine-learning-37ua698b.pdf

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

The energy in buildings is influenced by numerous factors characterized by non-linear multi-interrelationships. Consequently, the prediction of the energy performance of a building, in the presence of these factors, becomes a complex task. The work presented in this paper utilizes risk and sensitivity analysis and applies artificial neural networks (ANNs) to predict the energy performance of buildings in terms of primary energy consumption and CO2 emissions represented in the Building Energy Rating (BER) scale. Training, validation, and testing of the utilized ANN was implemented using simulation data generated from a stochastic analysis on the ‘Dwellings Energy Assessment Procedure’ (DEAP) energy model. Four alternative ANN models for varying levels of detail and accuracy are devised for fast and efficient energy performance prediction. Two fine-detailed models, one with 68 energy-related input factors and one with 34 energy-related input factors, offer quick and multi-factored estimations of the energy performance of buildings with 80 and 85% accuracy, respectively. Two low-detailed models, one with 16 and one with 8 energy-related input factors, offer less computationally intensive yet sufficiently accurate predictions with 92 and 94% accuracy, respectively.

Building energy performance prediction using neural networks

A building’s energy performance is a complex multi-dimensional metric consisting of a variety of parameters. Presented herein are the results of a stochastic analysis of the factors affecting a building’s energy performance. The analysis is based on the Dwelling Energy Assessment Procedure (DEAP) (amended for cooling loads) and the general guidelines prescribed by the European Energy Performance of Buildings (EPBD) Directive 2010/31/EU. Modifications to the DEAP model are made for investigating the effect of variable external weather conditions on a building’s energy performance, and to incorporate the additional energy requirement for cooling. Subsequently, a stochastic analysis for three dwelling types is performed to assess the impact of 68 factors on the energy performance of buildings, for 12 different regions in Europe. It is concluded that (1) the factors with the greatest impact on energy use are in descending order, the floor area, external weather conditions, dwelling’s envelope u value (roof, window, walls, and floors), the space heating system, ventilation, windows area and walls area; (2) the energy performance of a building follows a lognormal probability distribution function; (3) buildings in colder EU regions exhibit higher energy profiles and higher variability in their energy profiles than those in warmer regions.

Stochastic assessment of the energy performance of buildings

Reliability Assessment of BIM-Based Energy Efficiency Modeling

One advantage of neural ranking models is that they are meant to generalise well in situations of synonymity i.e. where two words have similar or identical meanings. In this paper, we investigate and quantify how well various ranking models perform in a clear-cut case of synonymity: when words are simply expressed in different surface forms due to regional differences in spelling conventions (e.g., color vs colour). We first explore the prevalence of American and British English spelling conventions in datasets used for the pre-training, training and evaluation of neural retrieval methods, and find that American spelling conventions are far more prevalent. Despite these biases in the training data, we find that retrieval models often generalise well in this case of synonymity. We explore the effect of document spelling normalisation in retrieval and observe that all models are affected by normalising the document's spelling. While they all experience a drop in performance when normalised to a different spelling convention than that of the query, we observe varied behaviour when the document is normalised to share the query spelling convention: lexical models show improvements, dense retrievers remain unaffected, and re-rankers exhibit contradictory behaviour.

On the Effects of Regional Spelling Conventions in Retrieval Models

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Papers

Building energy performance prediction using neural networks

Stochastic assessment of the energy performance of buildings

Reliability Assessment of BIM-Based Energy Efficiency Modeling

On the Effects of Regional Spelling Conventions in Retrieval Models