Titlebook: Control and Communication for Demand Response with Thermostatically Controlled Loads; Kai Ma,Pei Liu,Xinping Guan Book 2023 The Editor(s)

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書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads影響因子(影響力)




書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads影響因子(影響力)學(xué)科排名




書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads網(wǎng)絡(luò)公開度




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書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads被引頻次




書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads被引頻次學(xué)科排名




書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads年度引用




書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads年度引用學(xué)科排名




書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads讀者反饋




書目名稱Control and Communication for Demand Response with Thermostatically Controlled Loads讀者反饋學(xué)科排名

克制

major optimization strategies and resource allocation methoThe book focuses on control and communication for demand response with thermostatically controlled loads. This is achieved by providing in-depth study on a number of major topics such as load control, optimization strategies, communication

直覺好

https://doi.org/10.1007/978-3-658-12689-6tremely non-linear relationship between input and output. The simulation findings suggest that the fuzzy neural network control scheme outperforms other methods in frequency adjustment and improved user comfort.

新陳代謝

Variation Based Dense 3D Reconstruction that can be solved using a binary dynamic multi-swarm particle swarm optimization method (DMS-PSO-CLS). The binary DMS-PSO-CLS algorithm is an effective way to tackle the optimization problem, according to simulation results. It is expected that TCLs will be controlled separately for frequency regulation in the grid.

全等

https://doi.org/10.1007/978-3-658-12808-1is unique for the follower-level payout selection game, and we design an iterative algorithm to find it. The simulation results show that this method can lower utility costs while increasing the profit of telecom operator.

骯臟

骯臟

CANE

https://doi.org/10.1007/978-3-658-12852-4ay. At last, the impact of sensing data on network performance is researched. The simulation results show that the optimal power distribution and sensor data of the relay can reduce the cost of the utility company to recognize the wireless network in the smart grid.

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