Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)1

Responding to the Various Needs for Energy Usage
Electricity Optimization using Demand Prediction

TEPPEI TESHIMA*1

MASAZUMI OISHI*2TADASHI SUZUKI*2

HIDEKI HASHIMOTO*3

MASAYUKI MORIHARA*4

Large consumers including factories use various utilities such as energy (electricity, heat,fuel, etc.), water and compressed air, and they need to not only reduce energy costs, but to also
simultaneously solve many difficult issues including the reduction of CO2 emissions, theimprovement of production efficiency, the stabilization of operations and the shortage of skilled
engineers. To solve these issues, prediction technologies for determining how much and whenenergy will be needed in the future are essential. Mitsubishi Heavy Industries Ltd. (MHI) developedthe "power demand prediction system" which predicts factory power demand using its proprietaryAI, and this report introduces the system as well as case examples of demonstrations conducted in
MHI’s plant.

|1. Introduction
In recent years, the needs in energy supply and demand have been diversified through theliberalization of energy market, the increase in the use of renewable energy, the shift from
centralized energy systems to distributed energy systems, the management of BCP (BusinessContinuity Plan), environmental conservation through the promotion of carbon-free energy, etc.,
and these needs have been continuously changing. On the other hand, a stable energy supply isrequired for factory production activities, and efficient energy operation through energy saving
measures, the reduction of CO2 emissions, the reduction of energy cost and measures against theshortage of skilled engineers, are also required. Therefore, the need for prediction technologies
through which factory power demand can be accurately predicted has been increasing.
Digital technologies including AI and IoT (Internet of Things) have made remarkableprogress, and various kinds of systems using prediction technologies have been developed. TheENERGY CLOUDTM Service provides an energy solution for the prediction of factory power
demand using MHI's proprietary AI. General power demand predictions are made, in many cases,
based on the past actual values, weather information, etc. On the other hand, the system introducedin this report provides a highly-accurate prediction of power demand in association with factory
operational planning and production planning for the optimization of factory power supply anddemand.
This report introduces an overview of this system and the results of the demonstration of its
effects conducted at MHI's plants.

|2. Explanation of the power demand prediction system
The power demand prediction system employs the ensemble learning method*1, which is an
AI analysis method using MHI's proprietary technology. For highly-accurate prediction(1), thesystem automatically extracts the effective feature quantity from a large number of explanatory*1

Power & Energy Solution Business Planning Department, Power & Energy Solution Business Division, Power Systems*2Program Execution Manager, Power & Energy Solution Business Planning Department, Power & Energy Solution Business*3Manager, Power & Energy Solution Business Planning Department, Power & Energy Solution Business Division, Power Systems*4Manager, Power & Energy Solution Business Department, Power & Energy Solution Business Division, Power Systems

Division, Power Systems

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
2variables that are factors for prediction and classifies objective variables, i.e., targets to bepredicted, into clusters*2 before prediction. Based on the prediction system incorporating thismethod, the user interface and the system configuration were standardized. Then, the demonstration
was conducted onsite at MHI's plants, and the prediction system for factory power demand wasestablished.
*1
*2

2.1

Method of increasing versatility through the integration of the prediction results of multiple analysis results
A small group in which similar objects are collected from the population

Characteristics of the power demand prediction system

2.1.1 Power demand prediction in association with equipment operational planning
The power demand prediction system is constructed in the cloud, and prediction isconducted by analyzing the actual demand data accumulated by the customer and the datameasured in real time. The mechanism of the prediction system and the actions taken for theimprovement of prediction accuracy are presented in Figure 1.
Action (1): Customer inputs the explanatory variables, such as calendar data for factory
operation days and the operational plan for specific pieces of equipment
which have a correlation with power demand, through a web browser to thesystem.
Action (2): Power demand prediction is periodically corrected based on the demand datameasured in real time on the day and the production equipment operationaldata.
Action (3): Through continuous system operation, the AI conducts self-learning basedon the accumulated data and the analysis capability is improved.

Figure 1

Mechanism of the power demand prediction system

2.1.2 Service provision start in a short period
The system can be used with MHI Group's proprietary Netmation eFinderTM as astandard IoT tool for data acquisition which can be easily installed on the equipment. Therefor
the existing data and the measured data by Netmation eFinderTM allow the start of the power
demand prediction service in about two weeks.Even if no existing data is available in the caseof the start-up of a new factory, etc., the prediction service can be provided because the system
conducts analysis while conducting self-learning from the start of data measurement, and theprediction accuracy is improved with the passage of two or three months.
2.1.3 Prediction result display interface with consideration given to usability
The customer can check the prediction results anywhere by accessing the prediction
system from a web browser. In addition, two or more concerned parties can view the same
screen and results and share information, and quick and precise demand management, etc., canMitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)3

be realized. The power demand display pattern is illustrated in Figure 2.
When a date on the user interface is selected, the predicted value and the actual value aredisplayed on a graph, and the power demand value (average power usage per unit time) and thepower demand pattern can be visualized.
In the power demand prediction system, long-term, medium-term and short-term
predictions are conducted, and each prediction is properly used according to the purpose as
follows:- Long-term prediction: Prediction of annual demand tendency and maximum demandvalue based on factory operation days
- Medium-term prediction: Prediction of monthly or weekly demand tendency based on
operational plan
- Short-term prediction: Prediction of day's demand based on real-time data

Figure 2Results of prediction by the power demand prediction system

2.1.4 Demand peak reduction through power demand simulation
The power demand prediction system has a power demand simulation function using
explanatory variables as parameters and allows a series of studies toward the optimization offactory power demand. When the prediction shows that the demand peak is getting close to thecontract demand, measures can be taken beforehand by implementing a power demand
simulation using the equipment operational plan as a parameter and studying the operational
plan so that the peak is lowered. The demand simulation display pattern is given in Figure 3.

2.2

Effects of the power demand prediction system

By using this prediction system to manage factory power demand, the following effects canbe expected:- Power demand peak prediction was often conducted depending on experience andintuition. This prediction system visualizes and simulates the demand peak, thereby
allowing the operational plan to be specifically changed toward a peak shift or
peak-shaving and resulting in the improvement of operations and labor saving.
- By accumulating the demand data throughout the year, the annual peak value can bedetermined, and a study can be made toward the optimization of the contract demand forthe next year. In addition, the demand data can be used in planning for the introduction of
new utility supply facilities such as self-power generators, resulting in the reduction ofpower cost.
- The same prediction system is used by the production department and the powermanagement department, so that the latest actual demand, production planning, etc., can beshared, resulting in the implementation of factory-wide energy saving activities andincreased energy saving awareness.

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
4Figure 3

Simulation function of the power demand prediction system|3. Verification of effects by in-company demonstration
The developed power demand prediction system was introduced to four plants of MHI (MainPlant and Futami Plant of Kobe Shipyard & Machinery Works, Main Plant and Koyagi Plant ofNagasaki Shipyard & Machinery Works), which have different power supply and demand
characteristics, and the use of the prediction system was implemented at the actual plants. By using
the demand prediction system onsite where the actual power supply and demand operations wereconducted, the power demand prediction accuracy and the effects of the use of the prediction
system were verified.
As representative verification results of prediction accuracy, the results of the prediction at
12 AM and 9 PM can be seen in Table 1. The accuracy is defined as the difference between theprediction error rate (rate of the prediction error to true value) and 100%. Although the plants havedifferent scales of power demand, manufacture different products and have different surrounding
environments, an average prediction accuracy of about 85% was achieved in the prediction at 12AM. Compared with the average prediction accuracy at 12 AM, the average prediction accuracy at
9 PM was increased to about 90%. Therefore, it was verified that the periodical prediction
correction based on the day's real time data improved the accuracy. At the Futami Plant where theprediction accuracy was low, the operation of the electric furnace largely contributed to the power
demand. Therefore, the addition of the products being manufactured and the production quantity tothe explanatory variables to be input may improve the accuracy.
Table 1 Results of the verification of prediction accuracy at the plants of MHI
PeriodIntroduction day to the end of March 2018
Prediction accuracy

Kobe Shipyard &Machinery Works
Main Plant

Futami Plant

Nagasaki Shipyard &Machinery Works
Main Plant

Koyagi Plant

Prediction at 12 AM

86%

72%89%87%

Prediction at 9 PM

92%

80%94%

92%

In the future, the prediction system will be applied to the operation process at each plant(Figure 4) to verify the effectiveness of its use in power management operations.
Thus, it is considered that the optimization of operational and production plans of factories,
as well as the operational plans of utility facilities, not only reduces power cost, but also has othereffects such as increased production efficiency, the stabilization of operations and labor saving,
resulting in the optimization of energy supply and demand which is required in factories.

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 4 (December 2018)
5

Figure 4

Power management operational process using the power demand prediction system|4. Conclusion
MHI developed the "power demand prediction system" for the prediction of factory power
demand using its proprietary AI. With this system, we started the provision of services, based on
the previously described verification results, for both inside and outside of the company this year.
The power demand prediction system has the effects of the "reduction of peak power," the"reduction of power usage amount" and the "optimum operation of self-generation" as described inthis report. Furthermore, this system can be applied to demand prediction of not only power
demand, but also various utility demand such as steam, water and fuel. Going forward, we will
improve the prediction system and expand the application scope for the support of management
operations of various energy supplies at factories.
In the ENERGY CLOUDTM Service, the creation of commands for onsite operation based on
the prediction results, and in the future, automatic control of equipment according to its commands,
are positioned as a series of solution services and we will promote the provision of energy solutions
beginning with demand prediction as the starting point.
ENERGY CLOUDTM and all related marks and logos are registered trademarks of Mitsubishi Heavy Industries,
Ltd. in Japan and other countries.
Netmation eFinderTM and all related marks and logos are registered trademarks of Mitsubishi Hitachi Power
Systems, Ltd. in Japan.

References(1)

K. Wakasugi, et.al., Development of High-precision Demand Forecasting Technology for Factories,
Mitsubishi Heavy Industries Technical Review Vol. 55 No. 2