10,463 research outputs found

    Optimal greenhouse cultivation control: survey and perspectives

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    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

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    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

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    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

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    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

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    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

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    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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