10,463 research outputs found
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A decision support system for fostering smart energy efficient districts
The role of ICT is becoming prominent in tackling some of the urban societal challenges such as energy
wastage and increasing carbon emissions. In this context, the concept of DAREED aims to deliver an
integrated decision support system (DSS) to drive energy efficiency and low carbon activities at both a
building and district level. The main aim of this paper is to present the technical concept of the Best
Practices recommendation component of the DAREED system. This component seeks to compare and
identify existing best practices to recommend practical actions to various stakeholders (e.g. building
managers, citizens) in order to improve energy performance considering the global needs of a building.
This paper also discusses the context of the three field trial sites (based in UK, Spain and Italy) in which
the DAREED platform along with the best practices tool is to be tested and validated.This work evolved in the context of the project DAREED (Decision support Advisor for innovative
business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project cofunded
by the EC within FP7, Grant agreement no: 609082
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Constraint-based adaptation for complex space configuration in building services
In this paper an object-based CAD programming is used to take advantage of standardization to handle the schematic design, sizing and layout planning for ceiling mounted fan coil system in a building ceiling void. In order to deal with more complex geometry and real building size, we have used a hybrid approach combining case-based reasoning and constraint programming techniques. Very often, building services engineers use previous solutions and adapt them to new problems. Case-based reasoning mirrors this practical approach and did help us deal effectively with increasingly complex geometry. Our approach combines automation and interactivity. From the specification of the building 3D BIM model, our software prototype proceeds through four steps. First, the user divides the building into zones, each zone being defined by a geometrical primitive (i.e. rectangle zone, triangle zone, curved zone, etc.). Next, for each zone a similar case is retrieved from the case library. The retrieval process will generate a first incomplete 3D solution containing some inconsistencies. Next, the incomplete solution is adapted, using constraint programming techniques, to provide a consistent solution. Finally, distribution routes (i.e. ducts and pipes) are generated using constraint programming techniques. The 3D fan coil solution can be modified or improved by the designer, while providing further contribution by concentrating on interactivity. The project has been funded by the Engineering and Physical Sciences Research Council (EPSRC) in the UK
Optimal greenhouse cultivation control: survey and perspectives
Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well
Designing an occupancy flow-based controller for airport terminals
One of the most cost-effective ways to save energy in commercial buildings is through designing a dedicated controller for adjusting environmental set-points according occupancy flow. This paper presents the design of a fuzzy rule-based supervisory controller for reducing energy consumptions while simultaneously providing comfort for passengers in a large airport terminal building. The inputs to the controller are the time schedule of the arrival and departure of passenger planes as well as the expected number of passengers, zone global illuminance (daylight) and external temperature. The outputs from the controller are optimised temperature, airflow and lighting set-point profiles for the building. The supervisory controller was designed based on expert knowledge in MATLAB/Simulink, and then validated using simulation studies. The simulation results demonstrate significant potential for energy savings in the controller's ability to maintain comfort by adjusting set-points according to the flow of passengers.
Practical application : The systematic approach adopted here, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns like the restricted area of the airport terminal building
Multi-criteria energy and daylighting optimization for an office with fixed and moveable shading devices
This paper presents an optimization approach to design an external fixed shading device protecting an energy efficient office from high sun loads. The developed methodology takes into account heating, cooling and energy required for lighting appliances, along with the interaction with an internal moveable venetian blind for direct sunlight protection. The optimization process considers whole-year simulations performed with different software codes, specifically ESP-r for energy calculation and DAYSIM\uae for daylighting analysis, while the modeFRONTIER\uae tool synchronizes the simulations and drives the optimization for searching optimal solutions. The fixed shading device is a flat panel positioned parallel to the window and inclined by its horizontal axis and the optimization variables change the size, inclination and position of the device with respect to the building fa\ue7ade. Two exposures are considered, south and south-west, and the optimized results are reported as a Pareto front highlighting the performance of different solutions, comparing the energy and daylighting performance of the offic
Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network
The first step in identifying fruits on trees is to develop garden robots for different purposes
such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit
orchards and the unevenness of the various objects throughout it, usage of the controlled conditions
is very difficult. As a result, these operations should be performed in natural conditions, both
in light and in the background. Due to the dependency of other garden robot operations on the
fruit identification stage, this step must be performed precisely. Therefore, the purpose of this
paper was to design an identification algorithm in orchard conditions using a combination of video
processing and majority voting based on different hybrid artificial neural networks. The different
steps of designing this algorithm were: (1) Recording video of different plum orchards at different
light intensities; (2) converting the videos produced into its frames; (3) extracting different color
properties from pixels; (4) selecting effective properties from color extraction properties using
hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority
voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial
neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly
algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third
channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue
(LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation
intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution
and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation
criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled
“Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant
number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio
Engineering design applications of surrogate-assisted optimization techniques
The construction of models aimed at learning the behaviour of a system whose responses to inputs are expensive to measure is a branch of statistical science that has been around for a very long time. Geostatistics has pioneered a drive over the last half century towards a better understanding of the accuracy of such ‘surrogate’ models of the expensive function. Of particular interest to us here are some of the even more recent advances related to exploiting such formulations in an optimization context. While the classic goal of the modelling process has been to achieve a uniform prediction accuracy across the domain, an economical optimization process may aim to bias the distribution of the learning budget towards promising basins of attraction. This can only happen, of course, at the expense of the global exploration of the space and thus finding the best balance may be viewed as an optimization problem in itself. We examine here a selection of the state of-the-art solutions to this type of balancing exercise through the prism of several simple, illustrative problems, followed by two ‘real world’ applications: the design of a regional airliner wing and the multi-objective search for a low environmental impact hous
Minimizing Electricity Cost through Smart Lighting Control for Indoor Plant Factories
Smart plant factories incorporate sensing technology, actuators and control
algorithms to automate processes, reducing the cost of production while
improving crop yield many times over that of traditional farms. This paper
investigates the growth of lettuce (Lactuca Sativa) in a smart farming setup
when exposed to red and blue light-emitting diode (LED) horticulture lighting.
An image segmentation method based on K-means clustering is used to identify
the size of the plant at each stage of growth, and the growth of the plant
modelled in a feed forward network. Finally, an optimization algorithm based on
the plant growth model is proposed to find the optimal lighting schedule for
growing lettuce with respect to dynamic electricity pricing. Genetic algorithm
was utilized to find solutions to the optimization problem. When compared to a
baseline in a simulation setting, the schedules proposed by the genetic
algorithm can achieved between 40-52% savings in energy costs, and up to a 6%
increase in leaf area.Comment: IEEE IECON 202
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