書誌事項
- タイトル別名
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- STUDY ON REAL-TIME LOAD PREDICTION METHODS FOR HAVAC SYSTEM CONTROL
- クウチョウ システム ウンテン セイギョ オ タイショウ ト シタ ジツジカン
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抄録
First, importance of real-time load prediction and several kinds of optimization on HVAC system control are discussed. Then, six prediction models, ARIMA (Autoregressed Integrated Moving Average), EWMA (Exponential Weighted Moving Average), RLR (Recursive Linear Regression), ANN (Artificial Neural Network), KALMAN (Kalman filter) and FNN (Fuzzy Neural Network), are examined to compare their accuracy under situations where the all models use the same 3-month-long calculated load data and weather data in cooling and heating season. The results shows that the ANN model has the best prediction accuracy. It is confirmed that the ANN is a potential prediction model for practical utilization in HVAC system control.
収録刊行物
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- 日本建築学会計画系論文集
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日本建築学会計画系論文集 61 (484), 43-51, 1996
日本建築学会
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詳細情報 詳細情報について
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- CRID
- 1390001204783086336
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- NII論文ID
- 10004175434
- 110004654338
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- NII書誌ID
- AN10438548
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- ISSN
- 18818161
- 13404210
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- NDL書誌ID
- 3984815
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可