Application of Big Data and Internet of Things Technology in the Management of Financial Operating Income of Electric Power Enterprises: Aim. In order to effectively integrate and rationally allocate the complex information flow of people, finance, and materials in the daily operation of the enterprise and improve the daily operation efficiency and management effect of the enterprise, the author proposes a method for the application of big data and Internet of Things technology in the management of financial operating income of electric power enterprises. The method specifically includes an overview of the relevant theories of financial management based on ERP and an analysis of the needs of a power companys financial management system based on ERP so as to construct an evaluation index system for the financial management system. The experimental results show that the return on assets of the experimental enterprises has increased from 27.48 in 2015 to 55.18 in 2019, an increase of 27.7, and the profitability has been continuously enhanced. In 2019, the company achieved a net profit of 392 million yuan, an increase of about 4 times compared with the previous 77 million yuan in 2015. Conclusion. The application can further optimize the allocation of various resources of the enterprise and ensure the effective implementation of the long-term strategy and management goals of the enterprise on the basis of realizing the management needs of the enterprise.

How to perform accurate calculation of heat balance and quantitative analysis of energy efficiency for building clusters is an urgent problem to be solved to reduce building energy consumption and improve energy utilization efficiency. This article proposes a method for the heat balance calculation and energy efficiency analysis of building clusters based on enthalpy and humidity diagrams and applies it to the energy management of building clusters containing primary return air systems and heating pipe networks. Firstly, the basic structure and energy management principle of building clusters with a primary return air system and a heating pipe network were given, and the heat balance calculation and energy efficiency analysis method based on i-d diagram was proposed to realize the accurate calculation of heat load and the quantification of energy utilization. Secondly, the energy management model of the building cluster with a primary return air system and a heating pipe network was established to efficiently manage the indoor temperature and the heating schedule of ASHP, HN and HI. Finally, the proposed method was validated by calculation examples, and the results showed that the proposed method is beneficial for improving the energy economy and energy efficiency of building clusters.

The accurate prediction of building energy consumption is a crucial prerequisite for demand response (DR) and energy efficiency management of buildings. Nevertheless, the thermal inertia and probability distribution characteristics of energy consumption are frequently ignored by traditional prediction methods. This paper proposes a building energy consumption prediction method based on Bayesian regression and thermal inertia correction. The thermal inertia correction model is established by introducing an equivalent temperature variable to characterize the influence of thermal inertia on temperature. The equivalent temperature is described as a linear function of the actual temperature, and the key parameters of the function are optimized through genetic algorithm (GA). Using historical energy usage, temperature, and date type as inputs and future building energy comsuption as output, a Bayesian regression prediction model is established. Through Bayesian inference, combined with prior information on building energy usage data, the posterior probability distribution of building energy usage is inferred, thereby achieving accurate forecast of building energy consumption. The case study is conducted using energy consumption data from a commercial building in Nanjing. The results of the case study indicate that the proposed thermal inertia correction method is effective in narrowing the distribution of temperature data from a range of 24.5°C to 36.5°C to a more concentrated range of 26.5°C to 34°C, thereby facilitating a more focused and advantageous data distribution for predictions. Upon applying the thermal inertia correction method, the relative errors of the Radial Basis Function (RBF) and Deep Belief Network (DBN) decreases by 2.0% and 3.1% respectively, reaching 10.9% and 7.0% correspondingly. Moreover, with the utilization of Bayesian regression, the relative error further decreases to 4.4%. Notably, the Bayesian regression method not only achieves reduced errors but also provides probability distribution, demonstrating superiority over traditional methods.