In the design stage of energy systems in buildings, accurate load boundary conditions are the key to achieving energy supply and demand balance. Compared with the building cold and heat load, the generation of building electrical load has stronger randomness, and the current standard electrical load calculation method cannot reflect this feature. Therefore, this paper proposes a bottom-up high time resolution power load generation method for office buildings. Firstly, the non-homogeneous Markov chain is used to establish the random mobility model of personnel in office buildings, and the building electrical appliances are divided into four categories according to the different driving modes of personnel to electrical appliances in office buildings. Then, based on the personnel mobility model, the correlation between the use of electrical appliances in office buildings and the personnel in the room is established to construct the random power simulation model of different types of electrical appliances. Finally, the electric load of different types of electrical appliances is superimposed hourly to generate a random daily load curve. In order to verify the validity of the model, an office building is simulated and compared with the measured electrical load value. The verification results show that the model well reflects the daily distribution characteristics of electric load. The simulation value and the measured value are used for statistical analysis. The evaluation results show that the correlation between the simulation value and the measured value is high, which further illustrates the validity and accuracy of the model.
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Salk Inst Biol Studies, La Jolla, CA USA
Univ Calif San Diego, Biomed Sci Grad Program, La Jolla, CA USASalk Inst Biol Studies, La Jolla, CA USA
LeDuke, Deryn O.
Borio, Matilde
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Salk Inst Biol Studies, La Jolla, CA USASalk Inst Biol Studies, La Jolla, CA USA
Borio, Matilde
Miranda, Raymundo
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Salk Inst Biol Studies, La Jolla, CA USA
Univ Calif San Diego, Neurosci Grad Program, La Jolla, CA USASalk Inst Biol Studies, La Jolla, CA USA
Miranda, Raymundo
Tye, Kay M.
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Salk Inst Biol Studies, La Jolla, CA USA
Howard Hughes Med Inst, La Jolla, CA USA
Kavli Inst Brain & Mind, La Jolla, CA USA
Salk Inst Biol Studies, 10010 North Torrey Pines Rd, La Jolla, CA 92037 USASalk Inst Biol Studies, La Jolla, CA USA
机构:
Eurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, Italy
Univ Trento, Dept Civil Environm Mech Engn, Via Mesiano 77, I-38122 Trento, ItalyEurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, Italy
D'Alonzo, Valentina
Novelli, Antonio
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Eurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, ItalyEurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, Italy
Novelli, Antonio
Vaccaro, Roberto
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Eurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, ItalyEurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, Italy
Vaccaro, Roberto
Vettorato, Daniele
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Eurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, ItalyEurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, Italy
Vettorato, Daniele
Albatici, Rossano
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Univ Trento, Dept Civil Environm Mech Engn, Via Mesiano 77, I-38122 Trento, ItalyEurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, Italy
Albatici, Rossano
Diamantini, Corrado
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Univ Trento, Dept Civil Environm Mech Engn, Via Mesiano 77, I-38122 Trento, ItalyEurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, Italy
Diamantini, Corrado
Zambellia, Pietro
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Eurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, ItalyEurac Res, Inst Renewable Energy, Via A Volta 13-A, I-39100 Bolzano, Italy