Machine learning-based energy management system
View Patent ↗Disclosed is a machine learning-based energy management system, in which a system that manages and controls a power load module and a power source module on the basis of an energy management system (EMS) that performs controlling on the basis of energy is established so that a power source is able to operate a required load under an operating condition that maximizes energy efficiency and economic efficiency, and while the EMS operates in conjunction with a load control system (LCS) inducing an overall power generation efficiency increase and energy saving while controlling the charging and discharging of a battery according to the load size value of a generator, the power supply control signal of the power source corresponding to the required load of the power load module is calculated in real time by a control signal generation EMS algorithm that performs machine learning (ML) of load data/power data.
1 . A machine learning-based energy management system, the system comprising:
a power load module comprising a propulsion load and a service electrical load and configured to consume power supplied from outside;
a power source module comprising a fuel cell, a battery, and a generator and configured to supply power to the power load module;
a load management control module being connected to the power source module to perform status monitoring and operation control thereof, with the load management control module being configured to induce an overall power generation efficiency increase and energy saving while controlling charging and discharging of the battery according to a load size value of the generator; and
an energy management control module that is connected to the power load module to control power supply to the propulsion load and the service electrical load, and controls an operation of the power source module in conjunction with the load management control module, wherein the energy management control module allows a power supply control signal of the power source module corresponding to a required load of the power load module to be calculated in real time by a control signal generation energy management system (EMS) algorithm to which machine learning is applied, wherein the control signal generation EMS algorithm performs machine learning of load data of the power load module and power data of the power source module, and calculates the power supply control signal to increase output efficiency and energy saving efficiency of the power source module according to a load characteristic, the required load, and a load change of the power load module,
wherein the energy management control module comprises:
a data collection unit being connected to an electronic chart system (ECS) module, the power load module, and the power source module to cumulatively collect initial data for machine learning comprising ship operation data, required load data, actual operation load data, and status information of the power load module, and supply power data and status information of the power source module;
a valid data calculation unit being connected to the data collection unit to receive the initial data for machine learning, wherein the valid data calculation unit calculates valid data for machine learning by extracting only the initial data for machine learning that fall within a valid data range region set through a filtering algorithm,
wherein the filtering algorithm of the valid data calculation unit sets a numerical value region between a valid upper data limit (UDL) calculated from Equation 1 and a valid lower data limit (LDL) calculated from Equation 2 as the valid data range region,
wherein
UDL
=
m
0
+
W
d
s
2
-
s
[
1
-
(
1
-
s
)
2
k
]
LDL
=
m
0
-
W
d
s
2
-
s
[
1
-
(
1
-
s
)
2
k
]
and
m 0 is an input data average value, d is a standard deviation, W is a control width, and s is a smoothing parameter.
2 . The system of claim 1 , wherein the energy management control module further comprises:
a machine learning execution unit configured to set the control signal generation EMS algorithm according to an energy-based output prediction model (EB model) that estimates a power source output to increase the output efficiency and energy saving efficiency of the power source module, and configured to perform machine learning of the valid data for machine learning through the control signal generation EMS algorithm;
a power supply control signal calculation unit in which the power supply control signal of the power source module which is actively adjusted to the required load and load change of the power load module which is currently input in real time is calculated in real time by the control signal generation EMS algorithm; and
a power source operation control unit configured to receive the power supply control signal from the power supply control signal calculation unit to control the operation of the power source module.
3 . The system of claim 2 , wherein the machine learning execution unit and the power supply control signal calculation unit perform machine learning by receiving an output value, a voltage value, and a current value of the fuel cell of the power source module and then calculate the power supply control signal so that the fuel cell operates at an optimal output value within a range between a minimum output value and a maximum output value.
4 . The system of claim 2 , wherein the machine learning execution unit and the power supply control signal calculation unit perform machine learning by receiving a current value, a voltage value, and a remaining capacity value of the battery of the power source module and then calculate the power supply control signal so that operation stability of the power source module is increased.
5 . The system of claim 1 , wherein the power source module is provided with the battery charged by being connected to a hydrogen fuel cell and a shaft generator motor (SGM) system provided in an electric propulsion ship.
6 . The system of claim 1 , wherein the energy management control module operates in conjunction with a power management control module (PMS) previously installed on an electric propulsion ship.