Accurately estimating the distributed indoor thermal environmental parameters with limited sensors is crucial for indoor environmental quality and building energy efficiency. This study combines proper orthogonal deco...
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ISBN:
(纸本)9798350366907;9789887581581
Accurately estimating the distributed indoor thermal environmental parameters with limited sensors is crucial for indoor environmental quality and building energy efficiency. This study combines proper orthogonal decomposition and greedy algorithm for sensors layout's optimization in a large-space thermal environment. Firstly, choose the initial quantity and positions of sensors, and collect steady temperature field information based on the feasible range of environmental variables;Secondly, extract features from the collected dataset using proper orthogonal decomposition, and determine the optimal number of sensors based on the energy proportion of the ***;Thirdly, select the sensor positions iteratively based on the correlations of eigenvectors and sensors using greedy algorithm. A field experiment for performance validation is conducted using a matrix of 72 temperature sensors in a large cafeteria. By using the collected 27 snapshot datasets, the temperature field can be reconstructed by Linear Stochastic Estimation using only six optimal sensors (steady-state error: 0.2433, RMSE). The proposed POD-greedy optimization strategy is also compared with another heuristic inference method. The better performance shows great potential for engineering practice and applications.
This paper aims to implement a cloud-based monitoring DC microgrid system suitable for communities by integrating a simulated utility grid system (SUGS), battery energy storage system (BESS), solar power generation sy...
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ISBN:
(纸本)9798350351163;9798350351156
This paper aims to implement a cloud-based monitoring DC microgrid system suitable for communities by integrating a simulated utility grid system (SUGS), battery energy storage system (BESS), solar power generation system (SPGS), and cloud-based front-end monitoring interface and technology. Additionally, the paper utilizes Long Short-Term Memory (LSTM) model to predict the next day's load curve and employs a solar simulator to simulate the daily variations in solar irradiance. Furthermore, to enhance economic benefits during peak and off-peak time-of-use (TOU) pricing periods, this paper adopts the concept of local selection using a greedy algorithm to optimize energy allocation between SUGS, BESS, and SPGS through cloud computing. Finally, a derating case study is conducted through simulations and experiments to verify the economic value and feasibility of the proposed greedy algorithm in a community-based DC microgrid.
greedy algorithms are among the most elementary ones in theoretical computer science and understanding the conditions under which they yield an optimum solution is a widely studied problem. Greedoids were introduced b...
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greedy algorithms are among the most elementary ones in theoretical computer science and understanding the conditions under which they yield an optimum solution is a widely studied problem. Greedoids were introduced by Korte and Lovasz at the beginning of the 1980s as a generalization of matroids. One of the basic motivations of the notion was to extend the theoretical background behind greedy algorithms beyond the well-known results on matroids. Indeed, many well-known algorithms of a greedy nature that cannot be interpreted in a matroid-theoretical context are special cases of the greedy algorithm on greedoids. Although this algorithm turns out to be optimal in surprisingly many cases, no general theorem is known that explains this phenomenon in all these cases. Furthermore, certain claims regarding this question that were made in the original works of Korte and Lovasz turned out to be false only most recently. The aim of this paper is to revisit and straighten out this question: we summarize recent progress and we also prove new results in this field. In particular, we generalize a result of Korte and Lovasz and thus we obtain a sufficient condition for the optimality of the greedy algorithm that covers a much wider range of known applications than the original one.
Fix A subset of N. We study the iteration of the map which subtracts from its non-negative integer argument the largest element of {0,., min(A) - 1}. A not exceeding it. Letting r(A) (x) be the first point less than m...
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Fix A subset of N. We study the iteration of the map which subtracts from its non-negative integer argument the largest element of {0,., min(A) - 1}. A not exceeding it. Letting r(A) (x) be the first point less than min(A) in the orbit of x, we prove that, if every element of A is a multiple of min(A), then the asymptotic densities of the sets r(A)(-1)({t}) are equal for all t is an element of {0,., min(A) - 1}, and that the converse also holds if A is finite. In the case of A being infinite, we complement the above result with an explicit formula for the density of r(A)(-1)({0}) which holds under an appropriate condition on A.
The developing tendency of new energy cars in China is discussed. Firstly, four influencing indicators, namely, economy, policy, technology and after-sales service, were selected, and 11 years of data were collected a...
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This paper studies the estimation of the conditional density f (x, center dot) of Yi given Xi = x, from the observation of an i.i.d. sample (Xi, Yi) E Rd, i E {1, ... , n}. We assume that f depends only on r unknown c...
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This paper studies the estimation of the conditional density f (x, center dot) of Yi given Xi = x, from the observation of an i.i.d. sample (Xi, Yi) E Rd, i E {1, ... , n}. We assume that f depends only on r unknown components with typically r << d. We provide an adaptive fully-nonparametric strategy based on kernel rules to estimate f. To select the bandwidth of our kernel rule, we propose a new fast iterative algorithm inspired by the Rodeo algorithm (Wasserman and Lafferty, 2006) to detect the sparsity structure of f. More precisely, in the minimax setting, our pointwise estimator, which is adaptive to both the regularity and the sparsity, achieves the quasi-optimal rate of convergence. Our results also hold for (unconditional) density estimation. The computational complexity of our method is only O (dn log n). A deep numerical study shows nice performances of our approach.
With the modern logistics gradual transformed from labour-intensive to technology-intensive industry, artificial intelligence technology is widely used in logistics scheduling optimisation. As the warehouse management...
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With the modern logistics gradual transformed from labour-intensive to technology-intensive industry, artificial intelligence technology is widely used in logistics scheduling optimisation. As the warehouse management is one of the most important activities of logistics, the operation level of warehousing has a direct impact on the efficiency and total cost of the whole supply chain. Therefore, this paper takes the medical enterprise as the research object, investigates the current situation of its warehousing process, and finds out the existing problems. Then, FlexSim modal and greedy algorithms are used to simulate and optimise the operation process. After optimisation, the efficiency of medical storage has been improved obviously, and the operation cost has been reduced significantly, so as to realise the lean management of the whole medical supply chain.
With the rapid development of digitalization, it is particularly important to provide superstores with accurate information on market demand and trends, and to help them develop reasonable replenishment plans and pric...
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In the context of increasing flexible load represented by smart household appliances, this study aims to establish a demand response model of residential flexible load to minimize electricity cost and reduce grid load...
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In the context of increasing flexible load represented by smart household appliances, this study aims to establish a demand response model of residential flexible load to minimize electricity cost and reduce grid load variance. In this study, the flexible loads of residents are classified considering different load demand response modes, and demand response models are established for all kinds of loads. To make full use of residential electricity data, this paper established a user side flexible load multi -objective optimization scheduling model, the model for electricity cost and power grid load variance minimizing the objective function, and the safe operation of the meter and the adjustable resources as constraint, load and energy storage battery time-sharing electricity price for resident's flexible optimization scheduling. Finally, the greedy algorithm is use to calculate and analyze the model. The result show that the model is effective and feasible.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
A weak conical greedy algorithm is introduced with respect to an arbitrary positive complete dictionary in a Hilbert space;this algorithm gives an approximation of an arbitrary space element by a combination of dictio...
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A weak conical greedy algorithm is introduced with respect to an arbitrary positive complete dictionary in a Hilbert space;this algorithm gives an approximation of an arbitrary space element by a combination of dictionary elements with nonnegative coefficients. The convergence of this algorithm is proved and an estimate of the convergence rate for the elements of the convex hull of the dictionary is given.
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