Xia Wu

In rural locations, the application of clean heating technologies requires an appropriate design heating load. However, the variation characteristics of indoor temperatures in rural residential buildings are rarely taken into consideration by traditional techniques of calculating the design heating load, which may result in over- or under-design. As a result, a new method that took the uncertainty of the indoor temperature into account was presented to calculate the design heating load for rural residential buildings. First, for the “part-time, part-space” heating mode in rural residential buildings, an indoor temperature stochastic model was established to generate multiple indoor temperature scenarios; on the basis of this, heating loads under these scenarios were simulated and their probability distributions were counted; lastly, the design heating load was selected from the load probability distribution based on a predetermined confidence level. When the new method and the traditional method were compared, it was discovered that the new method can offer a more thorough guide to the determination of the design load value for the design of heating systems in rural residential buildings, while the traditional method’s result might not satisfy the reliability requirements.

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.