Evaluating energy supply service reliability for commercial air conditioning loads from the distribution network aspect

Cheng, Lin; Wan, Yuxiang; Tian, Liting; Zhang, Fang

doi:10.1016/j.apenergy.2019.113547

Cited by 25 publications

(3 citation statements)

References 35 publications

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“…The indoor environment holds paramount importance in people's lives, as approximately 90% of their time is spent indoors (Cheng et al, 2019). Beyond impacting the occupants' thermal comfort and physical health, the thermal conditions inside buildings have a direct bearing on energy consumption (Zhao et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Smart control system for optimal energy use in the oil and gas sector

Tasmurzayev,

Shinassylov,

Amangeldy

et al. 2023

Materials of International Practical Internet Conference “Challenges of Science”

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This paper delves into the development of a smart control system for air conditioning and heating within the oil, gas, and energy sectors. It explores techniques for creating an automated sensor monitoring framework and adjusting sensor settings for temperature and air conditioning management. The proposed intelligent HVAC (Heating, Ventilation, and Air Conditioning) system acquires live data from temperature and leak sensors, along with air conditioning units and switches. The paper introduces the system's structure, its operational principles, schematic representations of the control module, and the monitoring and management setup. The core component of the real-time monitoring and management system is realized through the utilization of the SCADA software for the control and supervision of sensor parameters.

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Section: Introductionmentioning

confidence: 99%

Smart control system for optimal energy use in the oil and gas sector

Tasmurzayev,

Shinassylov,

Amangeldy

et al. 2023

Materials of International Practical Internet Conference “Challenges of Science”

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“…The working state of these controllable loads can be directly controlled by a dispatching center or guided by economic measures (e.g., real-time electricity price). Generally, the power consumption of AC accounts for 30-55% of the whole building [9], EWH and refrigerators are also essential electrical appliances in every home, hence VES has a wide range of applications. For example, in residential communities, the controllable loads of AC, refrigerators, and EWH can be aggregated and regulated by smart meters to provide VES utility.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling Strategy of Building Integrated Photovoltaic Microgrid Considering Virtual Energy Storage

Zeng

et al. 2020

Applied Sciences

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Building integrated photovoltaic (BIPV) is one of the most efficient ways to utilize renewable energy in buildings. However, the stochastic characteristic of PV power generation and load challenges the optimal dispatch of the BIPV. This paper proposes an optimal scheduling strategy of BIPV microgrid considering virtual energy storage (VES), which intends to further improve the operating economy of a BIPV microgrid. Firstly, existing research findings and gaps in this research area were analyzed. Then, the mathematical model of each unit in the system was created, meanwhile, the air conditioner-based VES (ACVES) model and electric water heater-based VES (EWHVES) model are built by exploiting the heat/cold storage capability of building and the electric water heater, respectively. Next, the optimal scheduling model considering the VES is designed. Finally, the optimal scheduling model is solved by CPLEX to obtain the optimal scheduling strategy that reduces the operating cost of the system. Simulation results demonstrate that the proposed strategy can reduce the operating cost of the system. Furthermore, the VES shows the potential to replace the battery energy storage system.

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“…Among them, building energy consumption accounts for a large part of the total energy consumption [2]. In building energy consumption, air-conditioning systems occupy 30%-55% or even higher energy consumption [3]. Therefore, how to reduce the energy consumption of airconditioning systems has received great attention from both academics and industry.…”

Section: Introductionmentioning

confidence: 99%

Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

Chen¹,

2020

IEEE Access

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Accurate and reliable prediction of airconditioning load plays a significant role in prosumer energy management system (EMS), because airconditioning load accounts for a large proportion of the building's total energy consumption. This paper proposes a new meta ensemble learning method to realize short-term prediction of airconditioning load for prosumers. This method is a hybrid of meta ensemble learning and stacked auto-encoder (SAE). First, we design multiple different forecasting structures based on SAE to achieve point prediction of airconditioning loads. SAE is used to learn the deep features in airconditioning load data. Second, a new meta ensemble learning prediction model is proposed. Meta ensemble learning is used to learn the nonlinear features and invariant structures in data, and determine the coefficients of each SAE-based point forecaster. Finally, the prediction results of each point forecaster are aggregated and integrated to estimate the final airconditioning load prediction result. Airconditioning load data from a commercial building in Singapore are used to validate the feasibility and effectiveness of the proposed method, demonstrating that the proposed meta ensemble learning method is attractive in prosumer energy management. INDEX TERMS Airconditioning load, load prediction, meta learning, ensemble learning, prosumer energy management system.

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Evaluating energy supply service reliability for commercial air conditioning loads from the distribution network aspect

Cited by 25 publications

References 35 publications

Smart control system for optimal energy use in the oil and gas sector

Smart control system for optimal energy use in the oil and gas sector

Optimal Scheduling Strategy of Building Integrated Photovoltaic Microgrid Considering Virtual Energy Storage

Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

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