A Building Energy Consumption Prediction Method Based on Random Forest and ARMA
| Autor: | Beiyan Jiang, Nan Ma, Qianting Hao, Zhijin Cheng |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Consumption (economics)
Mathematical optimization Autoregressive model Computer science 020209 energy 0202 electrical engineering electronic engineering information engineering Mode (statistics) Benchmark (computing) Building energy Autoregressive–moving-average model 02 engineering and technology Random forest |
| Zdroj: | 2018 Chinese Automation Congress (CAC). |
| DOI: | 10.1109/cac.2018.8623540 |
| Popis: | To address the issue of building energy consumption optimization, a novel prediction method based on Random Forest (RF) and Auto Regressive Moving Average (ARMA) algorithm is presented in this paper. Considering building energy consumption was affected by the factors involving equipment usage, personnel information, and climate conditions, random forest method was introduced to build forecasting models based on the historical data. It includes working-day mode and non-working day mode according to operating characteristics. To address the switching challenge between working mode and non-working mode due to temporary work, ARMA model is introduced to serve as the benchmark to improve the presented model. Some case studies have been carried out to test the proposed method. The results indicate that it could provide good predictions under both stable and temporary conditions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|