Campus energy efficiency and electrical safety management method and system based on artificial intelligence technology
An artificial intelligence, electrical safety technology, applied in neural learning methods, artificial life, data processing applications, etc., can solve the lack of scene power consumption, it is difficult to uniformly monitor the actual use, the electrical fire warning and automatic management technology is not perfect, etc. problem, to achieve the effect of convenient energy consumption
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Embodiment 1
[0087] This embodiment provides a campus energy efficiency and electrical safety management method based on artificial intelligence technology, such as figure 1 shown, including the following steps:
[0088] S1: Collect front-end real-time energy consumption data and environmental information;
[0089] S2: Classify and store the front-end real-time energy consumption data, environmental information and historical energy consumption data collected in step S1;
[0090] S3: Establish a BP neural network model based on the real-time energy consumption data, and use the particle swarm optimization algorithm to optimize the BP neural network model to predict the energy consumption of the campus. According to the prediction of the energy consumption of the campus, the energy waste location and reason;
[0091] S4: Find the corresponding energy-consuming equipment according to the location of energy waste, and automatically control the working status of the energy-consuming equipmen...
Embodiment 2
[0112] The basis of this embodiment in Embodiment 1 is that step S3 also includes establishing a prediction error estimation model, and when the error of the prediction of campus energy consumption is within the allowable range, the current campus energy consumption situation and the working mode with the lowest campus energy consumption Comparing and forming the energy cost report to obtain the location and cause of energy waste; when the error of the campus energy consumption prediction is not within the allowable range, re-collect the front-end real-time energy consumption data and environmental information, re-establish the BP neural network model, and use the particle swarm algorithm Optimizing the BP neural network model.
[0113] The prediction error estimation model is specifically:
[0114] S221: The predicted data result s of the BP neural network model optimized by the particle swarm optimization algorithm f and historical energy use data s q For comparison, calcu...
Embodiment 3
[0149] A campus energy efficiency and electrical safety management system based on artificial intelligence technology, such as image 3 As shown, it includes front-end data acquisition module, system data platform, energy analysis module, historical energy consumption analysis module, automatic supervision module and electricity safety warning module, among which:
[0150] The front-end data acquisition module collects front-end real-time energy consumption data and environmental information, and transmits them to the system data platform in real time;
[0151] The system data platform classifies and stores the front-end real-time energy consumption data and environmental information collected in real time together with historical energy consumption data;
[0152] The energy analysis module uses the real-time energy consumption data stored on the system data platform to establish a BP neural network model, and uses the particle swarm optimization algorithm to optimize the BP neu...
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