In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes ...In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.展开更多
Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to hea...Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to heavy traffic in cellular networks. To alleviate the transmission workload, offloading techniques have been proposed, where a cellular network distributes some popular data items to other wireless networks, so that users can directly download these data items from the wireless network around them instead of the cellular network.In this paper, we design a Cost Saving Offloading System（CoSOS）, where the Internet of Things（IoT） is used to undertake partial data traffic and save more bandwidth for the cellular network. Two types of algorithms are proposed to handle the popular data items distribution among users. The experimental results show that CoSOS is useful in saving bandwidth and decreasing the cost for cellular networks.展开更多
基金supported by the National Science Foundation(NSF)grant ECCF 1936494.
文摘In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.
基金supported by the National Natural Science Foundation of China (Nos. 61300207, 61370084, and 61502116)
文摘Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to heavy traffic in cellular networks. To alleviate the transmission workload, offloading techniques have been proposed, where a cellular network distributes some popular data items to other wireless networks, so that users can directly download these data items from the wireless network around them instead of the cellular network.In this paper, we design a Cost Saving Offloading System（CoSOS）, where the Internet of Things（IoT） is used to undertake partial data traffic and save more bandwidth for the cellular network. Two types of algorithms are proposed to handle the popular data items distribution among users. The experimental results show that CoSOS is useful in saving bandwidth and decreasing the cost for cellular networks.
