CN-122019621-A - Heat energy use strategy recommendation method and device applied to heat energy demand scene

CN122019621ACN 122019621 ACN122019621 ACN 122019621ACN-122019621-A

Abstract

The invention relates to the technical field of intelligent management of energy sources, and discloses a heat energy use strategy recommendation method and a heat energy use strategy recommendation device applied to a heat energy demand scene, wherein historical energy consumption data and real-time operation data of a target heat energy demand scene are obtained; according to a preset thermal energy use strategy evaluation model, performing mode analysis on historical thermal energy use data and real-time operation data to generate a plurality of preliminary thermal energy use strategies suitable for a target thermal energy demand scene, outputting quantized expected benefit evaluation values of each preliminary thermal energy use strategy, determining a target recommendation strategy from the plurality of preliminary thermal energy use strategies according to all the quantized expected benefit evaluation values, and outputting the target recommendation strategy to the target thermal energy demand scene. Therefore, the method and the device can improve the accuracy of the recommendation of the thermal energy use strategy applied to the thermal energy demand scene.

Inventors

DU CHUANZHONG
LUO LING
RAN ZHAOYU
Ran Weikang

Assignees

安徽泰然信息技术有限公司

Dates

Publication Date: 20260512
Application Date: 20251231

Claims (10)

1. A thermal energy usage policy recommendation method applied to a thermal energy demand scenario, the method comprising: Acquiring historical energy consumption data and real-time operation data of a target heat energy demand scene; Performing mode analysis on the historical energy consumption data and the real-time operation data according to a preset thermal energy use strategy evaluation model to generate a plurality of preliminary thermal energy use strategies suitable for the target thermal energy demand scene, and outputting a quantized expected benefit evaluation value of each preliminary thermal energy use strategy; Determining a target recommendation strategy from a plurality of the preliminary heat energy usage strategies according to all the quantized expected benefit evaluation values; And outputting the target recommendation strategy to the target heat energy demand scene.
2. The thermal energy usage policy recommendation method applied to a thermal energy demand scenario according to claim 1, wherein the performing pattern analysis on the historical usage data and the real-time operation data according to a preset thermal energy usage policy evaluation model to generate a plurality of preliminary thermal energy usage policies applicable to the target thermal energy demand scenario, and outputting a quantized expected benefit evaluation value of each of the preliminary thermal energy usage policies comprises: Performing feature extraction on the historical energy consumption data to obtain an energy consumption feature mode of the target heat energy demand scene, wherein the energy consumption feature mode is used for representing heat energy usage habit and load change rule of the target heat energy demand scene in a historical period; Performing state analysis on the real-time operation data to obtain a current state characteristic mode of the target heat energy demand scene, wherein the current state characteristic mode is used for representing the operation working condition and the environment parameters of the target heat energy demand scene at the current moment; Based on the energy utilization characteristic mode and the current state characteristic mode, matching and generating a plurality of preparation heat energy utilization strategies from a preset strategy knowledge base; Calculating, for each of the preliminary thermal energy usage policies, a performance score of the preliminary thermal energy usage policy in at least one preset evaluation dimension, the preset evaluation dimension including at least a performance dimension reflecting a thermal energy utilization efficiency, a cost dimension reflecting a power consumption economy, and a reliability dimension reflecting a power consumption safety reliability; and carrying out fusion calculation on the expression scores of the preparation heat energy use strategies according to preset dimension weights to obtain quantitative expected benefit evaluation values of the preparation heat energy use strategies.
3. The thermal energy usage policy recommendation method applied to a thermal energy demand scenario according to claim 2, wherein the generating a plurality of preliminary thermal energy usage policies from a preset policy knowledge base based on the energy usage feature pattern and the current state feature pattern includes: generating a scene matching constraint condition according to the energy utilization characteristic mode and the current state characteristic mode, wherein the scene matching constraint condition is used for limiting a scene characteristic range which is suitable for a matched strategy; According to the scene matching constraint conditions, a plurality of candidate heat energy use strategies meeting the conditions are retrieved from a preset strategy knowledge base; for each candidate heat energy use strategy, calculating the comprehensive matching degree of the candidate heat energy use strategy, the energy use characteristic mode and the current state characteristic mode, wherein the comprehensive matching degree is used for quantifying the matching degree of the candidate heat energy use strategy and the target heat energy demand scene; and screening out a preset number of candidate strategies which are ranked at the front according to the comprehensive matching degree of all the candidate heat energy use strategies so as to obtain a plurality of preparation heat energy use strategies.
4. The thermal energy usage policy recommendation method applied to a thermal energy demand scenario according to claim 2, wherein for each of said preliminary thermal energy usage policies, said calculating a performance score of said preliminary thermal energy usage policy in at least one preset evaluation dimension comprises: Analyzing the preliminary heat energy use strategy to obtain the defined expected energy use period, power curve, equipment operation parameters and safety set values; comparing the expected energy utilization period, the power curve and the energy utilization characteristic mode, and calculating the matching score of the time coincidence degree and the load curve similarity as the expression score of the preliminary heat energy utilization strategy in the efficacy dimension; Based on the energy price and the equipment efficiency parameter contained in the current state characteristic mode, combining the equipment operation parameter, simulating and calculating the estimated energy consumption cost for executing the preliminary heat energy use strategy, and converting the estimated energy consumption cost into an economical score as the expression score of the preliminary heat energy use strategy in the cost dimension; And comparing the safety set value with a safety threshold value and a reliability specification contained in the current state characteristic mode, evaluating the coincidence degree of the safety set value and the reliability specification, and generating a safety coincidence degree score serving as a performance score of the strategy in the reliability dimension.
5. The thermal energy usage policy recommendation method applied to a thermal energy demand scenario according to claim 2, wherein the performing fusion calculation on the expression scores of the preliminary thermal energy usage policy according to a preset dimension weight to obtain a quantized expected benefit evaluation value of the preliminary thermal energy usage policy includes: acquiring dimension weight coefficients respectively preset for the efficiency dimension, the cost dimension and the reliability dimension; respectively carrying out standardization processing on the performance scores of the preparation heat energy use strategy in the efficiency dimension, the cost dimension and the reliability dimension, and mapping the performance scores to uniform scoring dimension; according to the dimension weight coefficient, carrying out weighted summation calculation on the performance scores of the performance dimension, the cost dimension and the reliability dimension which are subjected to standardization processing to obtain a weighted comprehensive score of the preliminary heat energy use strategy; and carrying out normalization processing on the weighted comprehensive scores, and converting the weighted comprehensive scores into a final quantized value in a preset score interval, wherein the final quantized value is used as a quantized expected benefit evaluation value of the preliminary heat energy use strategy.
6. The thermal energy usage policy recommendation method applied to a thermal energy demand scenario according to claim 2, wherein said determining a target recommendation policy from a plurality of said preliminary thermal energy usage policies based on all of said quantified expected benefit assessment values comprises: selecting a strategy with the quantized expected benefit evaluation value larger than or equal to a preset evaluation threshold value from all the preliminary heat energy use strategies as a reference strategy; comparing the performance scores of the reference strategy in the efficacy dimension, the cost dimension and the reliability dimension with preset dimension thresholds of corresponding dimensions respectively, and identifying at least one dimension in which the performance scores are smaller than the preset dimension thresholds of the corresponding dimensions as a target dimension; screening at least one strategy with the highest expression score on the target dimension from all other preliminary heat energy using strategies as a complementary strategy; extracting policy parameters related to the target dimension from the complementary policies; Generating a new candidate strategy based on the original parameters of the reference strategy and the strategy parameters of the complementary strategy, and calculating the quantized expected benefit evaluation value of the new candidate strategy; Comparing the quantized expected benefit evaluation value of the new candidate strategy with the quantized expected benefit evaluation values of all the preliminary heat energy use strategies, and selecting the strategy corresponding to the highest quantized expected benefit evaluation value as a target recommendation strategy.
7. The thermal energy usage policy recommendation method applied to a thermal energy demand scenario according to any one of claims 1-6, further comprising: Recording the target recommendation strategy output to the target heat energy demand scene and the corresponding quantized expected benefit evaluation value; monitoring and acquiring actual energy utilization result data generated after the target recommended strategy is actually executed in the target heat energy demand scene; Calculating an actual benefit achievement value of the target recommendation strategy based on the actual energy utilization result data; comparing the actual benefit achievement value with the quantized expected benefit evaluation value, and calculating an evaluation deviation value; if the estimated deviation value exceeds a preset allowable deviation threshold, generating a correction instruction of the thermal energy use strategy estimation model according to the actual energy use result data, the historical energy use data and the real-time operation data; the corrective instruction is executed to adjust at least one internal parameter of the thermal energy usage policy evaluation model used to generate the preliminary thermal energy usage policy or calculate the quantified expected benefit evaluation value.
8. A thermal energy usage policy recommendation device for use in a thermal energy demand scenario, the device comprising: The acquisition module is used for acquiring historical energy utilization data and real-time operation data of the target heat energy demand scene; The generation module is used for carrying out mode analysis on the historical energy consumption data and the real-time operation data according to a preset thermal energy use strategy evaluation model, generating a plurality of preliminary thermal energy use strategies suitable for the target thermal energy demand scene, and outputting a quantized expected benefit evaluation value of each preliminary thermal energy use strategy; a determining module, configured to determine a target recommendation policy from a plurality of the preliminary heat energy usage policies according to all the quantized expected benefit evaluation values; and the recommending module is used for outputting the target recommending strategy to the target heat energy demand scene.
9. A thermal energy usage policy recommendation device for use in a thermal energy demand scenario, the device comprising: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform the thermal energy usage policy recommendation method of any of claims 1-7 applied to a thermal energy demand scenario.
10. A computer storage medium storing computer instructions which, when invoked, are operable to perform the thermal energy usage policy recommendation method of any one of claims 1-7 applied to a thermal energy demand scenario.

Description

Heat energy use strategy recommendation method and device applied to heat energy demand scene Technical Field The invention relates to the technical field of intelligent management of energy, in particular to a heat energy use strategy recommendation method and device applied to a heat energy demand scene. Background In complex scenarios involving the use of thermal energy, such as industrial manufacturing, building environmental control, etc., the actual operation of the system often deviates from the original design or ideal state, resulting in frequent problems of high energy consumption, unexpected operation costs, challenges in equipment reliability, etc. Currently, the daily operational adjustments and operational decisions of such scenarios rely primarily on the observation of the monitoring data by the operator and the accumulation of personal experience. The method has the remarkable limitations that firstly, the method is highly dependent on personal experience and on-site judgment, knowledge is difficult to solidify, standardize and inherit, different experts possibly give suggestions with large differences and even contradictions, and secondly, manual analysis is difficult to process massive and multidimensional historical data and real-time data, and an optimized operation mode and potential association cannot be systematically identified, so that operation adjustment lacks accurate and prospective data support and is mostly represented as 'post-remediation'. It is important to propose a technical solution for improving the accuracy of the thermal energy usage policy recommendation applied to the thermal energy demand scenario. Disclosure of Invention The invention provides a heat energy use strategy recommendation method and a heat energy use strategy recommendation device applied to a heat energy demand scene, which can improve the accuracy of heat energy use strategy recommendation applied to the heat energy demand scene. In order to solve the technical problem, the first aspect of the present invention discloses a thermal energy usage policy recommendation method applied to a thermal energy demand scenario, the method comprising: Acquiring historical energy consumption data and real-time operation data of a target heat energy demand scene; Performing mode analysis on the historical energy consumption data and the real-time operation data according to a preset thermal energy use strategy evaluation model to generate a plurality of preliminary thermal energy use strategies suitable for the target thermal energy demand scene, and outputting a quantized expected benefit evaluation value of each preliminary thermal energy use strategy; Determining a target recommendation strategy from a plurality of the preliminary heat energy usage strategies according to all the quantized expected benefit evaluation values; And outputting the target recommendation strategy to the target heat energy demand scene. In an optional implementation manner, in a first aspect of the present invention, the performing, according to a preset thermal energy usage policy evaluation model, pattern analysis on the historical thermal energy usage data and the real-time operation data to generate a plurality of preliminary thermal energy usage policies applicable to the target thermal energy demand scenario, and outputting a quantized expected benefit evaluation value of each of the preliminary thermal energy usage policies includes: Performing feature extraction on the historical energy consumption data to obtain an energy consumption feature mode of the target heat energy demand scene, wherein the energy consumption feature mode is used for representing heat energy usage habit and load change rule of the target heat energy demand scene in a historical period; Performing state analysis on the real-time operation data to obtain a current state characteristic mode of the target heat energy demand scene, wherein the current state characteristic mode is used for representing the operation working condition and the environment parameters of the target heat energy demand scene at the current moment; Based on the energy utilization characteristic mode and the current state characteristic mode, matching and generating a plurality of preparation heat energy utilization strategies from a preset strategy knowledge base; Calculating, for each of the preliminary thermal energy usage policies, a performance score of the preliminary thermal energy usage policy in at least one preset evaluation dimension, the preset evaluation dimension including at least a performance dimension reflecting a thermal energy utilization efficiency, a cost dimension reflecting a power consumption economy, and a reliability dimension reflecting a power consumption safety reliability; and carrying out fusion calculation on the expression scores of the preparation heat energy use strategies according to preset dimension weights to obtain quantitative expected benef