Highlights Developed machine learning models for HVAC electricity consumption prediction.  Compared the performance of feed-forward back-propagation artificial neural network (ANN) with random forest (RF).  The ANN model performed marginally better than the RF model.  RF model can be used as a variable selection tool. AbstractEnergy prediction models are used in buildings as a performance evaluation engine in advanced control and optimisation, and in making informed decisions by facility managers and utilities for enhanced energy efficiency. Simplified and data-driven models are often the preferred option where pertinent information for detailed simulation are not available and where fast responses are required. We compared the performance of the widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity in prediction-for predicting the hourly HVAC energy consumption of a hotel in Madrid, Spain. Incorporating social parameters such as the numbers of guests marginally increased prediction accuracy in both cases. Overall, ANN performed marginally better than RF with root-mean-square error (RMSE) of 4.97 and 6.10 respectively. However, the ease of tuning and modelling with categorical variables offers ensemble-based algorithms an advantage for dealing with multi-dimensional complex data, typical in buildings. RF performs internal cross-validation (i.e. using outof-bag samples) and only has a few tuning parameters. Both models have comparable predictive power and nearly equally applicable in building energy applications.

Highlights  A systematic review protocol provides unbiased and meaningful meta-information  A direct model accuracy comparison across studies is meaningless  A taxonomy for an informed forecasting model's selection is proposed  Recommendations on writing electrical load forecasting related paper are given ABSTRACT Electricity forecasting is an essential component of smart grid, which has attracted increasing academic interest. Forecasting enables informed and efficient responses for electricity demand. However, various forecasting models exist making it difficult for inexperienced researchers to make an informed model selection. This paper presents a systematic review of forecasting models with the main purpose of identifying which model is best suited for a particular case or scenario. Over 113 different case studies reported across 41 academic papers have been used for the comparison. The timeframe, inputs, outputs, scale, data sample

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With increasing complexity of construction projects, a collaborative environment becomes essential to ensure effective communication during the project lifecycle.Conventional team collaboration raises issues such as the lack of trust; uncertainties regarding ownership and Intellectual Property Rights (IPRs); miscommunication; and cultural differences, among others. Additional issues can arise in relation to the generated data, including data loss, data inconsistency, errors, and liability for wrong or incomplete data. Furthermore, There is a shortage of studies that investigate collaboration practices, data management, and governance issues from a sociotechnical perspective. This study investigates the development of a BIM governance framework (G-BIM) with support of Cloud technologies, identifying effectiveness factors that guarantee successful collaboration. Semi-structured interviews were conducted with informed BIM experts in the UK, with the aim of: (i) discovering current trends in Information Communication Technologies (ICT) and team collaboration during construction projects; (ii) exploring barriers to BIM adoption; (iii) exploring the role of BIM-related standards; (iv) consulting BIM experts to develop a Cloud-based BIM governance solution to tackle team collaboration on BIM-based projects; and (v) investigating the role of Cloud in supporting BIM governance research and development. The findings reveal several BIM adoption barriers and issues directly influencing team collaboration. The key findings led to the development of a BIM governance framework (G-BIM). The purpose of the G-BIM framework is to present and summarise effective factors resulting in successful governance and a collaborative BIM approach, to support the future development of a Cloud-based BIM governance platform. The G-BIM framework comprises three main components: (i) actors and team, (ii) data management and ICT, and (iii) processes and contracts. Furthermore, the study reveals the high potential of Cloud technologies to advance current BIM governance solutions, because of its performance capabilities, accessibility, storage, and scalability.

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Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences.Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.