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

Novel computational technique for determining depth using the Bees Algorithm and blind image deconvolution

In the past decade the Scanning Electron Microscope (SEM) has taken on a significant role in the micro-nano imaging field. A number of researchers have been developing computational techniques for determining depth from SEM images. Depth from Automatic Focusing (DFAF) is one of the most popular depth computation techniques used for SEM. However, images captured with SEM may be distorted and suffer from problems of misalignment due to internal and external factors such as interaction between electron beam and surface of sample, lens aberrations, environmental noise and artefacts on the sample. Distortion and misalignment cause computational errors in the depth determination process. Image correction is required to reduce those errors. In this study the proposed image correction procedure is based on Phase Correlation and Log-Polar Transformation (PCLPT), which has been extensively used as a preprocessing stage for many image processing operations. The computation process of PCLPT covers the pixel level interpolation process but it cannot deal with sub-pixel level interpolation errors. Hence, an image filtering stage is necessary to reduce the error. This enhanced PCLPT was also utilised as a pre-processing step for DFAF which is the first contribution of this research. Although DFAF is a simple technique, it was found that the computation involved becomes more complex with image correction. Thus, the priority to develop a less complicated and more robust depth computation technique for SEM is needed. This study proposes an optimised Blind Image Deconvolution BID) technique using the Bees Algorithm for determining depth. The Bees Algorithm (BA) is a swarm-based optimisation technique which mimics the foraging behaviour of honey bees. The algorithm combines exploitative neighbourhood search with explorative global search to enable effective location of the globally optimal solution to a problem. The BA has been applied to several optimisation problems including mechanical design, job shop scheduling and robot path planning. Due to its promise as an effective global optimisation tool,the BA has been chosen for this work. The second contribution of the research consists of two improvements which have been implemented to enhance the BA. The first improvement focuses on an adaptive approach to neighbourhood size changes. The second consists of two main steps. The first step is to define a measurement technique to determine the direction along which promising solutions can be found. This is based on the steepness angle mimicking the direction along which a scout bee performs its figure-of-eight waggle dance during the recruitment of forager bees. The second step is to develop a hybrid algorithm combining BA and a Hill Climbing Algorithm (HCA) based on the threshold value of the steepness angle. The final contribution of this study is to develop a novel technique based on the BA for optimising the blurriness parameter with BID for determining depth. The techniques proposed in this study have enabled depth information in SEM images to be determined with 68.23 % average accuracy

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

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 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 an ideal candidate 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 the minimization 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 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

Preserving prosumer privacy in a district level smart grid

This study presents the anonymization of consumer data in a district-level smart grid using the k-anonymity approach. The data utilized in this study covers the demographic information and associated energy consumption of consumers. The anonymization process is implemented at the prosumer level, considering their importance in sharing flexibility and distributed generation at the low voltage grid, and the fact that they need to interact with each other and the grid while keeping their data private. The proposed approach is tested under three anonymization scenarios: prosecutor, journalist, and marketer. The smart grid data are investigated mostly under the prosecutor scenario with three risk levels: lowest, medium and highest. The results of the k-anonymity approach are compared to k-map and k-map + k-anonymity. No difference has been found between the three investigated approaches for the selected data set. Since, the aim of the k-anonymity is to not transform the information about any individual record among those k-1 individuals, the recorded type and the number of attributes play a key role in the anonymization process. One of the risks is the using continuous attributes in the anonymization process which may cause the information lose in the anonymization process such as near real-time energy consumptions. Hence we have focused on to anonymization of the consumers' demographic information, rather than their energy consumption

Usability evaluation of a web-based tool for supporting holistic building energy management

This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken

ANN-GA smart appliance scheduling for optimized energy management in the domestic sector

Smart scheduling of energy consuming devices in the domestic sector should factor in clean energy generation potential, electricity tariffs, and occupants’ behaviour (i.e. interactions with their appliances). The paper presents an ANN–GA (Artificial Neural Network / Genetic Algorithm) smart appliance scheduling approach for optimized energy management in the domestic sector. The proposed approach reduces energy demand in “peak” periods, maximizes use of renewable sources (PV and wind turbine), while reducing reliance on grid energy. Comprehensive parameter optimization has been carried out for both ANN and GA to find the best combinations, resulting in optimum weekly schedules. The proposed artificial intelligence techniques involve a holistic understanding of (near) real-time energy demand and supply within a domestic context to deliver optimized energy usage with minimum computational needs. The solution is stress-tested and demonstrated in a four bedroom house with grid energy usage reduction by 10%, 25%, and 40%, respectively

User centered neuro-fuzzy energy management through semantic-based optimization

This paper presents a cloud-based building energy management system, underpinned by semantic middleware, that integrates an enhanced sensor network with advanced analytics, accessible through an intuitive Web-based user interface. The proposed solution is described in terms of its three key layers: 1) user interface; 2) intelligence; and 3) interoperability. The system’s intelligence is derived from simulation-based optimized rules, historical sensor data mining, and a fuzzy reasoner. The solution enables interoperability through a semantic knowledge base, which also contributes intelligence through reasoning and inference abilities, and which are enhanced through intelligent rules. Finally, building energy performance monitoring is delivered alongside optimized rule suggestions and a negotiation process in a 3-D Web-based interface using WebGL. The solution has been validated in a real pilot building to illustrate the strength of the approach, where it has shown over 25% energy savings. The relevance of this paper in the field is discussed, and it is argued that the proposed solution is mature enough for testing across further buildings

Honey Bees Inspired Optimization Method: The Bees Algorithm

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem