Mitsubishi Heavy Industries Technical Review Vol. 55 No. 2 (June 2018)
1Development of High-precision DemandForecasting Technology for Factories

KAZUYUKI WAKASUGI*1 KATSUAKI MORITA*2YUSUKE YASHIRO*3

RYUJI IKEDA*4

TEPPEI TESHIMA*5TOMOYUKI ENOMOTO*6

By operation with an efficient combination of multiple utility facilities such as turbines,
boilers and engines, distributed power supply using renewable energy and a power storage systemsuch as ESS, effects such as a reduction of CO2 emissions, a reduction of energy cost through theoptimization of power contracts and a reduction of fuel cost and an increase in profits through theselling of power can be expected. There are many technologies required for the realization of suchan operation, and one of the key elements is demand forecasting technology. In this report, theforecasting method and system that Mitsubishi Heavy Industries, Ltd. (MHI) developed with afocus on factory demand forecasting and a case example where the developed system was applied
to forecast the power demand in its factory are described.

|1. Introduction
Large consumers such as large-scale factories have been faced with the need for more
efficient energy operations in order to correspond to the changes in the energy environment due toglobal environmental issues and electricity deregulation. But under the present situation, it isespecially difficult for factories requiring multiple kinds of energy (electric power, heat, water, etc.)
to pursue efficient energy operations because of the following issues:(1) While forecasting various types of factory demand that change from moment to moment due
to variable factors such as states of production and facilities and weather conditions and theamount of power generated by distributed power supply, the operation plan for the utility
facilities must be reviewed according to needs and the supply of energy must be continued
without interruption.
(2) Even if a method of making an operation plan is established, the state of operation changes
with the degradation of facilities and change of products with the passage of time, resulting
in obsolescence of the method.
(3) Since the labor force is declining due to declining birthrate, etc., the labor resources at thefactory must be concentrated in the production section, which is the factory core. Therefore,
it is difficult to continuously conduct the maintenance as described above.
To respond to these needs, we have been promoting the provision of an energy solution
business called ENERGY CLOUD® Service(1). This service allows the highly-accurate forecastingof various types of demand using our proprietary technology described in this report and canprovide support for the planning of the efficient operation of utility facilities which leads to a
reduction of energy costs (such as fuel cost and man-hours). This forecasting system has a functionof relearning the model as appropriate, enabling continuous use of the system without
obsolescence. In addition, since the adjustment of parameters is incorporated into the algorithm,
manual adjustment is not required.
*1*2*3*4*5*6

EPI Department, ICT Solution Headquarters
Chief Staff Manager, EPI Department, ICT Solution HeadquartersCIS Department, ICT Solution Headquarters
ICT Project Department, ICT Solution Headquarters
Power & Energy Solution Business Planning Department, Power & Energy Solution Business Division, Power Systems
Chief Staff Manager, Power & Energy Solution Business Planning Department, Power & Energy Solution BusinessDivision, Power Systems

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 2 (June 2018)2|2. Developed method
2.1

Problems in the conventional method

The forecasting system using conventional methods suitable for demand forecasting using
the linear regression method, such as the seasonal autoregressive integrated moving average(SARIMA) model, had the following problems:(1) The forecasting accuracy is low. In the autoregressive method, for example, any data item
other than demand cannot be set as an explanatory variable, and therefore, operation plans,
weather information, etc., cannot be used as explanatory variables. In addition, there is thepossibility that by using some explanatory variable values, a predicted value which differs
significantly from the actual performance value will result. This low forecasting accuracy
leads to a reduction of the effect of the solution using the forecast results. So, a highforecasting accuracy is desirable.
(2) Manual tuning is required. For example, parameters such as the number of dimensions need
to be adjusted for each case. In addition, even if tuning is completed, a review is required
when the number of explanatory variables is changed or the content is changed. Whenmanual work is required, it may interfere with the development or make continuous
operation difficult due to a delay in introduction, etc. Therefore, it is desirable to establish aforecasting model with the minimum amount of work

2.2

Characteristics of the developed method(1)High forecasting accuracy
The method adopts an ensemble learning method in which our proprietary technology isincorporated. In this method, an effective number of features is automatically extracted from alarge number of explanatory variables, and therefore, operation plans and weather information
can be incorporated without issue. In addition, as shown in Figure 1, the target variables aredivided into clusters before the forecasting is conducted, allowing appropriate forecastingvalues to be more easily obtained. The verification results for the actual data showed that thedeveloped method can reduce errors by as much as 50% compared with the conventional
method (Section 2.3), and as a result of the method being applied to five plants, it has been
confirmed that it is effective because an average of about 90% accuracy was obtained(Section 4).
(2) No parameter adjustment required
In the developed method, parameter adjustment is not required in the entire model, except
for the number of ensembles which is decided according to the computational resources.
Concerning the number of divided clusters, for example, an optimal division number is
automatically decided based on the verification using the past data. In addition, if anyexplanatory variable item is changed, the forecasting model can be automatically restudied
according to the changed item. Thus, in this method, the parameters can be flexibly adapted to
any change of input data (Section 3). With this function, in the verification example for 5 plantsof our company, the operation could be started in about two weeks for each plant (Section 4).

Figure 1

Forecasting in combination with clusteringMitsubishi Heavy Industries Technical Review Vol. 55 No. 2 (June 2018)3

2.3

Comparison with conventional method

In order to verify the effectiveness of the developed method, we performed a comparison ofthe accuracy between the developed method and the SARIMA at two sites of our company (Site K,Site O) as one example.
(1) Verification conditions
The verification conditions are depicted in the upper part of Table 1. The learning data,
verification data, and forecasting conditions are common. For explanatory variables, in theSARIMA which is an autoregressive model, only the past demand data can be used in principle,
while in this developed method, calendar data and weather data are also used.
Table 1

Verification conditions and verification results
Site K

Item
Learning data
Verification data
Forecastingcondition
Explanatory
variable(2)Site O
Conventional
Developed
Conventional
methodmethodmethod2015/7/1 to 2016/2/23
2016/2/24 to 2016/3/31
Forecasting the demand for the period from 12:00 a.m. to 12:00 p.m. of the next dayat 11:00 a.m. on the previous day (every 30 minutes, in 48 segments)- Past demand- Past demand- Past demand- Past demand- Calendar information- Calendar information- Weather information- Weather information
Developedmethod

Verification results
The verification results can be found in Figure 2. The average number of errors in theconventional method is evaluated as 100% at each site. The error value is calculated from theminimum root mean square error (RMSE). The results showed that the number of errors wassubstantially reduced by about 50% at Site K and by as much as 30% at Site O. Thus, theeffectiveness of this developed method was proved. For reference, the forecasting waveform of
the developed method is presented in Figure 3. It is considered that the reasons why thedeveloped method has such a large effect compared with the SARIMA are that the explanatory
variables such as calendar data and weather data can be freely added, and that clustering
reduces the number of days with extreme deviations. From this result, it can be expected thatthe developed method allows forecasting with higher accuracy than the conventional method.

Figure 2 Comparison between the conventional method
and the developed method

Figure 3 Results of forecasting using the developed method

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 2 (June 2018)4|3. Forecasting system
Next, the forecasting system using the developed method is described. An outline of thetypical system configuration of the demand forecasting system is given in Figure 4. This system isconstructed on the cloud and automatically collects the facility operational data and time-series datasuch as open information according to the settings and uses them in forecasting. In addition, thesetting of off-days and the facility operation plans can be input in a web browser by users asappropriate.

Figure 4

Outline of the demand forecasting system

With these data, the demand forecasting system conducts demand forecasting for a maximumof one year. Because the long-term, medium-term and short-term concepts can be used together asshown in Figure 5, the system is available in varied applications to meet each purpose of the users.
If the user changes the operation plan, etc., the system detects the changes and updates theforecasting results. Specifically, as an annual operation plan is refined monthly or weekly, theforecasting results are changed and the accuracy is increased accordingly. The system also allows
updating of the day's demand forecasting based on the latest operational data once an hour.
Therefore, even if the operation is not conducted according to the plan, the forecasting results canfollow the operation. Furthermore, this system has a simulation function as previously described.
Therefore, when the facility operation plan is changed, the demand according to the plan can besimulated. Thus, the system is also used as a study tool for operation adjustment based on thedemand forecasting results.

Figure 5

Forecasting period of the demand forecasting system|4. Demonstration example
The results of the application of the developed forecasting system to seven factories in ourcompany are given in Table 2. The accuracy is defined as the difference from 100% after theRMSE is standardized by the average value of true values. As a representative example, theaccuracy value for the day's 24-hour power demand forecasting at 12:00 a.m. is indicated. Theresults showed that the average accuracy for the seven factories was high at about 90%, althoughthere were some differences by factory.

Mitsubishi Heavy Industries Technical Review Vol. 55 No. 2 (June 2018)
5Table 2

Demonstration results at in-house factories

TargetPlant A
Plant B
Plant C
Plant D
Plant E

SystemPeriod of introduction
12 days
10 days
9 days16 days
12 days

Accuracy(to December 2017)
87.8%
90.1%
89.8%
92.0%
93.7%

The system has started a short-period operation of about 2 weeks including the fieldadjustment, and the standardization has been fully developed through the in-house demonstration.
This system has also been applied to the forecasting of steam and water demand in addition topower demand, and the application to factories of other companies has also been promoted.

|5. Conclusion
In this report, our proprietary method that allows a higher level of forecasting accuracy thanthe conventional method, the forecasting system using the method and the demonstration example
were described.
Through the development of this system, the high-accuracy forecasting of various types ofpower demand and the amount of power generated by distributed power supply, etc., which was
one of bottlenecks in the implementation of an efficient energy operation method at large factories,
can be provided through the ENERGY CLOUD® Service. At present, the application of this system
to internal and external projects is being promoted.
This system can be applied to demand forecasting for engineering utilities such as steam andwater, in addition to electric power. Through demonstrations, we continuously conduct verificationof the forecasting accuracy of the system, and in the future, we will develop a technology thatenables the creation of an optimal system operation plan from the perspective of operation andmaintenance by combining the system with the abnormality detecting technology which has been
simultaneously demonstrated.
ENERGY CLOUD® and related marks and logos are registered trademarks of Mitsubishi
Heavy Industries, Ltd.

References
1.

News Release from Mitsubishi Heavy Industries, Ltd.
http://www.mhi.co.jp/news/story/1703015844.html