Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks
As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus b...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/24/8038 |
| _version_ | 1797381289151561728 |
|---|---|
| author | Donghun Lee Jongeun Kim Suhee Kim Kwanho Kim |
| author_facet | Donghun Lee Jongeun Kim Suhee Kim Kwanho Kim |
| author_sort | Donghun Lee |
| collection | DOAJ |
| description | As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model’s performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption. |
| first_indexed | 2024-03-08T20:49:17Z |
| format | Article |
| id | doaj.art-ec5fe0ee0c504b25b79f10e79ff7f81b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-08T20:49:17Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ec5fe0ee0c504b25b79f10e79ff7f81b2023-12-22T14:05:53ZengMDPI AGEnergies1996-10732023-12-011624803810.3390/en16248038Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural NetworksDonghun Lee0Jongeun Kim1Suhee Kim2Kwanho Kim3Industrial and Management Engineering, Incheon National University, Incheon 22012, Republic of KoreaIndustrial and Management Engineering, Incheon National University, Incheon 22012, Republic of KoreaIndustrial and Management Engineering, Incheon National University, Incheon 22012, Republic of KoreaIndustrial and Management Engineering, Incheon National University, Incheon 22012, Republic of KoreaAs the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model’s performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption.https://www.mdpi.com/1996-1073/16/24/8038smart energy systemsbuilding energy management systemsenergy forecastingdeep recurrent neural networks |
| spellingShingle | Donghun Lee Jongeun Kim Suhee Kim Kwanho Kim Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks Energies smart energy systems building energy management systems energy forecasting deep recurrent neural networks |
| title | Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks |
| title_full | Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks |
| title_fullStr | Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks |
| title_full_unstemmed | Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks |
| title_short | Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks |
| title_sort | comparison analysis for electricity consumption prediction of multiple campus buildings using deep recurrent neural networks |
| topic | smart energy systems building energy management systems energy forecasting deep recurrent neural networks |
| url | https://www.mdpi.com/1996-1073/16/24/8038 |
| work_keys_str_mv | AT donghunlee comparisonanalysisforelectricityconsumptionpredictionofmultiplecampusbuildingsusingdeeprecurrentneuralnetworks AT jongeunkim comparisonanalysisforelectricityconsumptionpredictionofmultiplecampusbuildingsusingdeeprecurrentneuralnetworks AT suheekim comparisonanalysisforelectricityconsumptionpredictionofmultiplecampusbuildingsusingdeeprecurrentneuralnetworks AT kwanhokim comparisonanalysisforelectricityconsumptionpredictionofmultiplecampusbuildingsusingdeeprecurrentneuralnetworks |