## 5 Conclusions  
Due to the intermittency and adjustability of popular renewable energy such as solar and wind power, they are unsuitable for baseload power supply substitute. In recent years, Japan has been promoting power generation from municipal waste for regional utilization. Waste is considered a stable power source with relatively low CO\({}_{2}\) emissions. However, several factors have hindered cost-effective waste power generation. Some of these factors are: (1) difficulty in securing a waste quantity suitable for incineration, (2) low power generation efficiency, and (3) difficulty in power adjustment. In addition, CO\({}_{2}\) emissions are calculated imprecisely because the waste input fluctuation over the years was not considered.  
Responding to the above challenges and supporting better waste power generation utilization, this study took a large-scale waste treatment plant in Iga City, Mie Prefecture, and a solar power generation plant located in Izumi City, Osaka Prefecture as case study plants. Using actual data from the case plants, this study has:  
* Developed a prediction method by analyzing the power data of operation plan line, always in operation line, waste power generation, and solar power generation. For solar power generation, the average error was 125.3 kW by predicting the amount of solar radiation based on the weather forecast.
* Calculated the CO\({}_{2}\) emission factor by time of the day from the data of power company A analysis. This study also developed a method to predict the CO\({}_{2}\) emission factor by time of the day with a prediction accuracy of 0.81 by predicting the solar power generation amount in power company A.  
Results showed that by formulating an operation plan for the operation plan line using CO\({}_{2}\) emission factor by time of the day and a method for predicting power load and power generation, CO\({}_{2}\) emissions could be reduced by 20.95% in weeks with high solar power generation and by 8.30% in weeks with low high solar power generation.  
The limitation of this study was that the actual operation plan could not be acquired (this study only estimated it using the power data due to company confidentiality). Furthermore, personnel constraints were not considered in the proposed operation plan.  
When constructing an operation plan of a similar power generation facility with the purpose of CO\({}_{2}\) emissions reduction, it is necessary to accurately predict the following three parameters: (1) power generation, (2) power demand, and (3) CO\({}_{2}\) emission factor. Thus, while the prediction method developed in this study can be used as a benchmark, future works analyzing other facilities must employ the actual parameters from the specific plants. Additionally, the target power load facilities in this study are limited to industrial facilities in the industrial sector. Actual users in the region include residential and commercial facilities, hospitals, and public facilities. The findings of this study can be used to establish  
Figure 23: Comparison of solar power generation and CO\({}_{2}\) emission factor made one week and two days in advance.  
a forecasting method for waste treatment facilities and to plan electricity use that minimizes CO2 emissions as a region.  
All the authors have contributed substantially to the work reported. Conceptualization, D.Y. and H.O.; methodology, D.Y., R.K. and Y.O.; formal analysis, D.Y., R.K. and Y.O.; investigation, D.Y., R.K. and Y.O.; data curation, D.Y., R.K. and Y.O.; writing--original draft preparation, D.Y. and H.O.; writing--review and editing, D.Y., R.K., Y.O., A.H.P. and H.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this research are available on request from the corresponding author.  
The authors declare no conflict of interest.