Modeling of Complex Hydronic Systems for Energy Efficient Operation

Energy requirements for heating and cooling of residential, commercial and industrial spaces constitute a major fraction of end use energy consumed. Centralized systems such as hydronic networks are becoming increasingly popular to meet those requirements. Energy efficient operation of such systems requires intelligent energy management strategies, which necessitates an understanding of the complex dynamical interactions among its components from a mathematical and physical perspective. In this work, concepts from linear graph theory are applied to model complex hydronic networks. Further, time-scale decomposition techniques have been employed to obtain a more succinct representation of the overall system dynamics. Lastly, the usefulness of the proposed model for energy efficient operation of the system through advanced control techniques has been discussed.

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Energy efficient biomorphic pulse information coding in electronic neurons for the entrance unit of the neuroprocessor

Tyumen State University Herald Physical and Mathematical Modeling Oil Gas Energy ◽

10.21684/2411-7978-2019-5-3-186-212 ◽

2019 ◽

Vol 5 (3) ◽

pp. 186-212 ◽

Cited By ~ 1

Author(s):

Alexander D. Pisarev

Keyword(s):

Neural Networks ◽

Energy Efficiency ◽

Energy Efficient ◽

Information Transfer ◽

Biological Systems ◽

Technological Parameters ◽

Input Unit ◽

Efficient Operation ◽

Electric Circuits ◽

Electronic Neuron

This article studies the implementation of some well-known principles of information work of biological systems in the input unit of the neuroprocessor, including spike coding of information used in models of neural networks of the latest generation.<br> The development of modern neural network IT gives rise to a number of urgent tasks at the junction of several scientific disciplines. One of them is to create a hardware platform — a neuroprocessor for energy-efficient operation of neural networks. Recently, the development of nanotechnology of the main units of the neuroprocessor relies on combined memristor super-large logical and storage matrices. The matrix topology is built on the principle of maximum integration of programmable links between nodes. This article describes a method for implementing biomorphic neural functionality based on programmable links of a highly integrated 3D logic matrix.<br> This paper focuses on the problem of achieving energy efficiency of the hardware used to model neural networks. The main part analyzes the known facts of the principles of information transfer and processing in biological systems from the point of view of their implementation in the input unit of the neuroprocessor. The author deals with the scheme of an electronic neuron implemented based on elements of a 3D logical matrix. A pulsed method of encoding input information is presented, which most realistically reflects the principle of operation of a sensory biological neural system. The model of an electronic neuron for selecting ranges of technological parameters in a real 3D logic matrix scheme is analyzed. The implementation of disjunctively normal forms is shown, using the logic function in the input unit of a neuroprocessor as an example. The results of modeling fragments of electric circuits with memristors of a 3D logical matrix in programming mode are presented.<br> The author concludes that biomorphic pulse coding of standard digital signals allows achieving a high degree of energy efficiency of the logic elements of the neuroprocessor by reducing the number of valve operations. Energy efficiency makes it possible to overcome the thermal limitation of the scalable technology of three-dimensional layout of elements in memristor crossbars.

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Buildings

10.1093/oso/9780198802297.003.0003 ◽

2017 ◽

Author(s):

Peter Rez

Keyword(s):

Heat Transfer ◽

Heat Pump ◽

Energy Efficient ◽

Energy Use ◽

Fossil Fuel ◽

Local Climate ◽

Heating And Cooling ◽

The Difference ◽

As If

Most of the energy used by buildings goes into heating and cooling. For small buildings, such as houses, heat transfer by conduction through the sides is as much as, if not greater than, the heat transfer from air exchanges with the outside. For large buildings, such as offices and factories, the greater volume-to-surface ratio means that air exchanges are more significant. Lights, people and equipment can make significant contributions. Since the energy used depends on the difference in temperature between the inside and the outside, local climate is the most important factor that determines energy use. If heating is required, it is usually more efficient to use a heat pump than to directly burn a fossil fuel. Using diffuse daylight is always more energy efficient than lighting up a room with artificial lights, although this will set a limit on the size of buildings.

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Energy Efficient Operation of Massive MIMO in Industrial Internet of Things Networks

IEEE Internet of Things Journal ◽

10.1109/jiot.2020.3039236 ◽

2020 ◽

pp. 1-1

Author(s):

Byung Moo Lee

Keyword(s):

Internet Of Things ◽

Energy Efficient ◽

Massive Mimo ◽

Industrial Internet Of Things ◽

Efficient Operation ◽

Industrial Internet

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Energy efficient fixed-cluster architecture for wireless sensor networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-192177 ◽

2021 ◽

Vol 40 (5) ◽

pp. 8727-8740

Author(s):

Rajvir Singh ◽

C. Rama Krishna ◽

Rajnish Sharma ◽

Renu Vig

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Aggregation ◽

Energy Efficient ◽

Search Algorithm ◽

Cluster Formation ◽

Wireless Sensor ◽

Efficient Operation ◽

Cluster Architecture ◽

Dynamic Cluster

Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.

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