Artificial intelligent manufacturing & production energy-saving system and method thereof

• Main Authors: KAO, WEN-HUANG, SHAW, RUEY-SHIANG
• Format: Patent
• Language: Chinese; English
• Published: 11.11.2023
• Subjects: AIR-CONDITIONING, AIR-HUMIDIFICATION, VENTILATION, USE OF AIRCURRENTS FOR SCREENING; ALARM SYSTEMS; BLASTING; CALCULATING; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER; COMPUTING; CONTROL OR REGULATING SYSTEMS IN GENERAL; CONTROLLING; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRICITY; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; GENERATION; HEATING; LIGHTING; MECHANICAL ENGINEERING; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS; ORDER TELEGRAPHS; PHYSICS; RANGES; REGULATING; SIGNALLING; SIGNALLING OR CALLING SYSTEMS; SYSTEMS FOR STORING ELECTRIC ENERGY; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR; VENTILATING; WEAPONS

An artificial intelligent manufacturing & production energy-saving system is provided, which includes a monitoring module and a prediction module. The monitoring module collects the historical electricity consumption data and the historical temperature data of an area. The prediction module receives the historical electricity consumption data, the historical temperature data and the temperature forecasting data of the area. The prediction module executes a pre-processing process to preprocess the historical electricity consumption data and the historical temperature data. Then, the prediction module performs a training model based on XGboost algorithm to execute a training process according to the temperature forecasting data of a prediction period, all historical electricity consumption data and historical temperature data before the prediction period so as to generate an electricity consumption prediction result of the prediction period. Afterward, the prediction module generating an evaluation result accor