ML MODEL MANAGEMENT DEVICE AND ML MODEL MANAGEMENT METHOD

• Main Author: KONO YASUTAKA
• Format: Patent
• Language: English; Japanese
• Published: 16.05.2023
• Subjects: CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER; ELECTRICITY; GENERATION; SYSTEMS FOR STORING ELECTRIC ENERGY

To appropriately determine the requirement of a relearning of an ML model.SOLUTION: In a management calculator 5000, a control plan generation part 5120 generates a first control plan based on a RE power generation amount predicted by an estimation using an ML model 5320 and a prediction value of a power consumption. An apparatus control part 5130 controls a control object apparatus by using the first control plan. Further, the control plan generation part 5120 generates a second control plan on the basis of the RE power generation amount or an actual value of the power consumption in the case where an evaluation that an accuracy of the estimation is deteriorated is made by a relearning requirement determination part 5140. The relearning requirement determination part 5140 determines whether or not a negative difference occurs against a second control result when the control object apparatus is controlled by using the second control plan in the first control result when controlling the control object apparatus by using the first control plan, and determines that a relearning of the ML model 5320 is required when it is determined that the negative difference occurs.SELECTED DRAWING: Figure 3 【課題】ＭＬモデルの再学習の要否を適切に判断する。【解決手段】管理計算機５０００において、制御計画生成部５１２０は、ＭＬモデル５３２０を用いた推論により予測されたＲＥ発電量や消費電力量の予測値に基づいて第１の制御計画を生成し、機器制御部５１３０は、第１の制御計画を用いて制御対象機器を制御する。また、制御計画生成部５１２０は、再学習要否判定部５１４０により推論の精度が劣化していると評価された場合に、ＲＥ発電量や消費電力量の実測値に基づいて第２の制御計画を生成する。再学習要否判定部５１４０は、第１の制御計画を用いて制御対象機器を制御したときの第１の制御結果において、第２の制御計画を用いて制御対象機器を制御したときの第２の制御結果に対する負の差が生じるか否かを判断し、負の差が生じると判断した場合はＭＬモデル５３２０の再学習が必要と判定する。【選択図】図３