9,642 research outputs found

    Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition

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    In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about a building design process at micro-urban scale and strategies are defined to make the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphore) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are developed from building-simulation-assisted computational experiments, aimed to choose the form and position of a small building in a plot. Those indicators link the sharing of structure between subspaces ("commonality") to recursive recombination, measured as freshness of the search wake and novelty of the search moves. The aim of these indicators is to measure the relative effectiveness of decomposition-based design moves and create efficient block searches. Implications of a possible use of these indicators in genetic algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table

    A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

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    In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

    Minimizing water and energy consumptions in water and heat exchange networks.

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    This study presents a mathematical programming formulation for the design of water and heat exchangers networks based on a two-step methodology. First, an MILP (mixed integer linear programming) procedure is used to solve the water and energy allocation problem regarding several objectives. The first step of the design method involves four criteria to be taken into account., ie, fresh water consumption (F1), energy consumption (F2), interconnection number (F3) and number of heat exchangers (F4). The multiobjective optimization Min [F1, F2] is solved by the so-called ɛ-constraint method and leads to several Pareto fronts for fixed numbers of connections and heat exchangers. The second step consists in improving the best results of the first phase with energy integration into the water network. This stage is solved by an MINLP procedure in order to minimize an objective cost function. Two examples reported in the dedicated literature serve as test bench cases to apply the proposed two-step approach. The results show that the simultaneous consideration of the abovementioned objectives is more realistic than the only minimization of fresh water consumption. Indeed, the optimal network does not necessarily correspond to the structure that reaches the fresh water target. For a real paper mill plant, energy consumption decreases of almost 20% as compared with previous studies

    Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

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    A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

    Analysis of Influencing Factors of Green Building Energy Consumption Based on Genetic Algorithm

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    With the advancement of modernization, high energy consumption buildings can no longer meet the needs of social development. Under the background of low carbon and energy saving, the development of green buildings has become the only way, but its energy-saving design effect needs to be further studied. Aiming at lighting and energy consumption, this study carried out multi-factor optimization analysis based on genetic algorithm on factors such as windowing ratio, wall heat transfer coefficient, window heat transfer coefficient, window transmittance and roof insulation coefficient. Firstly, the theory and technical scheme of applying data mining technology to solve the energy-saving design problems of different buildings are proposed and implemented, including the design of new and existing buildings, as well as the determination of decisive parameters and non-decisive parameters. Secondly, computer simulation and theoretical analysis are used to optimize the analysis of the building scheme, so as to find the optimal design range of each influencing factor and the optimal design method of green low-energy building. Multi-factor optimization theory and genetic algorithm principle are summarized, and the heat transfer coefficient of external wall and window of the building is selected as the optimization variable, so as to achieve low energy consumption and enclosure cost of the building. Aiming at better thermal comfort, an optimization model was established. Finally, through empirical research, an energysaving plan was designed, and genetic algorithm was used to obtain the optimal solution for maximizing the incremental benefits obtained by unit input incremental cost. The results indicate that the ideal incremental benefits come from a reasonable and effective combination of technologies, mainly from air conditioning systems and lighting systems; the setting of the benchmark return rate will directly affect the optimization effect of energy-saving plans, providing decision-makers with the optimal combination of energy-saving technologies
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