State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Building thermal load prediction through shallow machine learning and deep learning

Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty

A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost

Due to reducing the reliance of buildings on fossil fuels, Passive House (PH) is receiving more and more attention. It is important that integrated optimization of passive performance by considering energy demand, cost and thermal comfort. This paper proposed a set three-stage multi-objective optimization method that combines redundancy analysis (RDA), Gradient Boosted Decision Trees (GBDT) and Non-dominated sorting genetic algorithm (NSGA-II) for PH design. The method has strong engineering applicability, by reducing the model complexity and improving efficiency. Among then, the GBDT algorithm was first applied to the passive performance optimization of buildings, which is used to build meta-models of building performance. Compared with the commonly used meta-model, the proposed models demonstrate superior robustness with the standard deviation at 0.048. The optimization results show that the energy-saving rate is about 88.2% and the improvement of thermal comfort is about 37.8% as compared to the base-case building. The economic analysis, the payback period were used to integrate initial investment and operating costs, the minimum payback period and uncomfortable level of Pareto frontier solution are 0.48 years and 13.1%, respectively. This study provides the architects rich and valuable information about the effects of the parameters on the different building performance

Efficiency and Optimization of Buildings Energy Consumption: Volume II

This reprint, as a continuation of a previous Special Issue entitled “Efficiency and Optimization of Buildings Energy Consumption”, gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms that allow control systems to be improved with the aim of reducing housing sector energy consumption

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Intelligent energy management system in buildings

Energy management systems have become one of the most significant concepts in the power energy area, due to the dependency of nowadays human’s lifestyle on electrical appliances and increment of energy demand during the past decades. From a general perspective, the total energy consumption by humans can be divided into three main economic sectors, namely industry, transportation, and buildings. Based on recent studies, the buildings present the largest share of consumption, standing for approximately 40% of the total consumption. This fact makes buildings energy management the most important component of energy management. On another hand, according to the variety of different types of buildings and several existing consumption appliances, the management of energy consumption in the building becomes a challenging problem. The main goal of a building energy management system is to control the energy consumption of the building by considering several facts, such as current and estimated consumption and generation, the energy price and comfort of the users. Due to the complexity of this management and limitations of available information, most of the existing systems focus on optimizing the consumption value and the cost of the energy with less consideration of the comforts and habits of the users. Moreover, the context of decision-making is also not sufficiently explored. However, the energy management in the building can be designed based on an intelligent system which has the knowledge to estimate the comforts and needs of the users and acts based on this awareness. This work studies and develops an intelligent energy management system for buildings energy consumption. This system receives the historical data of the building and uses a set of artificial intelligence techniques as well as several designed rulesets and acts as a recommender system. The goal of the generated recommendations by this system is to attune the usage of the electrical appliances of the building by comforts and habits of the residents while considering the price of the electricity market and the current context. Results show that the system enables users to obtain a comfortable environment in the building in the most affordable way.Nas últimas décadas, a dependência do estilo de vida na elevada utilização de dispositivos elétricos e grande consumo energético, faz com que os sistemas de gestão de energia sejam um dos conceitos mais relevantes no setor energético. Numa perspetiva geral, o total da energia consumida divide-se essencialmente em três setores económicos: industrial, transporte e edifícios. Os edifícios têm a maior representatividade, correspondendo aproximadamente a 40% do consumo total. Assim, a gestão energética em edifícios é a componente com maior importância nesta área. Por outro lado, devido à variedade dos diferentes tipos de edifícios e dispositivos de consumo, a gestão do consumo de energia nos edifícios apresenta desafios. O objetivo principal de um sistema de gestão energética em edifícios consiste em controlar o consumo energético no edifício, considerando diversos fatores, tais como o consumo e produção atuais, a sua estimativa, o preço de mercado e conforto dos seus utilizadores. Perante a complexidade desta gestão e das limitações da informação disponível, a maioria dos sistemas tem foco na otimização do consumo e os seus custos, tendo em menor consideração o conforto e hábito dos utilizadores. Além disso, o contexto da tomada de decisão não é devidamente explorado, enquanto a gestão energética em edifícios pode ser baseada num sistema inteligente, cujo conhecimento pode estimar o conforto e necessidades dos seus utilizadores, e assim atuar com base nessa consciência. Este trabalho estuda e desenvolve um sistema inteligente para a gestão do consumo de energia em edifícios. O sistema recebe o histórico de dados de um edifício, e utiliza um conjunto de técnicas de inteligência artificial e conjuntos de regras, funcionando como um sistema de recomendações. O objetivo das recomendações geradas pelo sistema é adaptar os dispositivos elétricos do edifício ao conforto e hábitos dos utilizadores enquanto são considerados o preço de mercado e o contexto atual. Os resultados demonstram que o sistema permite aos utilizadores obter um ambiente confortável no edifício, da forma mais económica possível