An ANN-GA semantic rule-based system to reduce the gap between predicted and actual energy consumption in buildings

This paper addresses the endemic problem of the gap between predicted and actual energy performance in public buildings. A system engineering approach is used to characterize energy performance factoring in building intrinsic properties, occupancy patterns, environmental conditions, as well as available control variables and their respective ranges. Due to the lack of historical data, a theoretical simulation model is considered. A semantic mapping process is proposed using principle component analysis (PCA) and multi regression analysis (MRA) to determine the governing (i.e., most sensitive) variables to reduce the energy gap with a (near) real-time capability. Further, an artificial neural network (ANN) is developed to learn the patterns of this semantic mapping, and is used as the cost function of a genetic algorithm (GA)-based optimization tool to generate optimized energy saving rules factoring in multiple objectives and constraints. Finally, a novel rule evaluation process is developed to evaluate the generated energy saving rules, their boundaries, and underpinning variables. The proposed solution has been tested on both a simulation platform and a pilot building - a care home in the Netherlands. Validation results suggest an average 25% energy reduction while meeting occupants' comfort conditions

A virtual collaborative platform to support building information modeling implementation for energy efficiency

There is increased interest in complying with the new regulations and policies associated with the climate change. In particular industries such as the AEC (Architecture, Engineering and Construction) industry seek to find new strategies and practices for facilitating sustainability but also new regulations to improve efficiency at the building level. Institutions and industrial bodies are now in the process of alignment with new legislative stipulations regarding carbon emissions with wider reflection into environment, social and economic models. At building level such strategies refer to decarbonisation and energy efficiency supported with data driven techniques enriched with virtual collaboration and optimization methods. The increased interest of the research community in Building Information Modeling (BIM) has facilitated numerous solutions ranging from digital products, information retrieval, and optimization techniques all aiming at addressing energy optimization and performance gap reduction. In this paper we present how a virtual collaborative system can be efficiently used for implementing BIM based energy optimization for controlling, monitoring buildings and running energy optimization, greatly contributing to creating a BIM construction community with energy practices. The solution described, known as energy-bim.com platform, disseminates energy efficient practices and community engagement and provides support for building managers in implementing energy efficient optimization plans

Information and Communications Technologies (ICTs) for energy efficiency in buildings: Review and analysis of results from EU pilot projects

Information and Communications Technologies (ICTs) can play a potential role in improving the energy performance of buildings by the implementation of effective solutions that take advantage of the energy interactions between all the elements included in a building. A revision of the 105 pilots implemented or under implementation in 18 projects in the area of ICTs for energy efficiency in buildings located in 23 European countries, through 88 cities with different types of climates, buildings and technologies have been carried out through documentary and field analysis of the energy, economic and social project results. These results have been extrapolated to assess the potential energy savings which could be expected at the EU level by implementing the solutions proposed by the projects. By the implementation of the different ICT solutions, buildings have achieved more than 20% energy savings. Pilots have demonstrated that the effectiveness of the ICT solution does not depend directly on the different climates where the solutions are implemented, but on several factors, such as the level of motivation, perceived thermal comfort, quality of social interaction and communication and ICT support

Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities

Federating cloud systems for collaborative construction and engineering

The construction industry has undergone a transformation in the use of data to drive its processes and outcomes, especially with the use of Building Information Modelling (BIM). In particular, project collaboration in the construction industry can involve multiple stakeholders (architects, engineers, consultants) that exchange data at different project stages. Therefore, the use of Cloud computing in construction projects has continued to increase, primarily due to the ease of access, availability and scalability in data storage and analysis available through such platforms. Federation of cloud systems can provide greater flexibility in choosing a Cloud provider, enabling different members of the construction project to select a provider based on their cost to benefit requirements. When multiple construction disciplines collaborate online, the risk associated with project failure increases as the capability of a provider to deliver on the project cannot be assessed apriori. In such uncontrolled industrial environments, “trust” can be an efficacious mechanism for more informed decision making adaptive to the evolving nature of such multi-organisation dynamic collaborations in construction. This paper presents a trust based Cooperation Value Estimation (CoVE) approach to enable and sustain collaboration among disciplines in construction projects mainly focusing on data privacy, security and performance. The proposed approach is demonstrated with data and processes from a real highway bridge construction project describing the entire selection process of a cloud provider. The selection process uses the audit and assessment process of the Cloud Security Alliance (CSA) and real world performance data from the construction industry workloads. Other application domains can also make use of this proposed approach by adapting it to their respective specifications. Experimental evaluation has shown that the proposed approach ensures on-time completion of projects and enhanced..

A smart forecasting approach to district energy management

This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers’ employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems