A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling
Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/2/443 |
| _version_ | 1798042427067465728 |
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| author | Sungwoo Park Jihoon Moon Seungwon Jung Seungmin Rho Sung Wook Baik Eenjun Hwang |
| author_facet | Sungwoo Park Jihoon Moon Seungwon Jung Seungmin Rho Sung Wook Baik Eenjun Hwang |
| author_sort | Sungwoo Park |
| collection | DOAJ |
| description | Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads. |
| first_indexed | 2024-04-11T22:35:23Z |
| format | Article |
| id | doaj.art-cf3907c08b334d7384342b7b300b3af5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:35:23Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-cf3907c08b334d7384342b7b300b3af52022-12-22T03:59:14ZengMDPI AGEnergies1996-10732020-01-0113244310.3390/en13020443en13020443A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power SchedulingSungwoo Park0Jihoon Moon1Seungwon Jung2Seungmin Rho3Sung Wook Baik4Eenjun Hwang5School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSchool of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSchool of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaDepartment of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, KoreaDepartment of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, KoreaSchool of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, KoreaSmart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads.https://www.mdpi.com/1996-1073/13/2/443short-term load forecastingtwo-stage forecasting modelcombined cooling heating and powerenergy operation planeconomic analysis |
| spellingShingle | Sungwoo Park Jihoon Moon Seungwon Jung Seungmin Rho Sung Wook Baik Eenjun Hwang A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling Energies short-term load forecasting two-stage forecasting model combined cooling heating and power energy operation plan economic analysis |
| title | A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling |
| title_full | A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling |
| title_fullStr | A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling |
| title_full_unstemmed | A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling |
| title_short | A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling |
| title_sort | two stage industrial load forecasting scheme for day ahead combined cooling heating and power scheduling |
| topic | short-term load forecasting two-stage forecasting model combined cooling heating and power energy operation plan economic analysis |
| url | https://www.mdpi.com/1996-1073/13/2/443 |
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