A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
District heating systems load forecasting: a deep neural networks model based on similar day approach
Accurate heat load forecasting is an important issue to ensure the reliable and efficient operation of a district heating system. In this paper, a hybrid model that combines similar day (SD) selection and Deep Neural Networks (DNNs) to construct SD-DNNs model for short-term load forecasting (STLF) is presented. A new Euclidean Norm (EN) weighted by eXtreme Gradient Boosting (XGBoost) is used to evaluate the similarity between the forecasting day and historical days. In this EN, the outdoor temperature, wind force and day-ahead load are simultaneously considered. And eight features are chosen as inputs of the DNNs to predict the heat load. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are used as accuracy evaluation indexes. The experimental results demonstrate that the SD-DNNs model can accurately forecast the heat load.
District heating systems load forecasting: a deep neural networks model based on similar day approach
Accurate heat load forecasting is an important issue to ensure the reliable and efficient operation of a district heating system. In this paper, a hybrid model that combines similar day (SD) selection and Deep Neural Networks (DNNs) to construct SD-DNNs model for short-term load forecasting (STLF) is presented. A new Euclidean Norm (EN) weighted by eXtreme Gradient Boosting (XGBoost) is used to evaluate the similarity between the forecasting day and historical days. In this EN, the outdoor temperature, wind force and day-ahead load are simultaneously considered. And eight features are chosen as inputs of the DNNs to predict the heat load. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) are used as accuracy evaluation indexes. The experimental results demonstrate that the SD-DNNs model can accurately forecast the heat load.
District heating systems load forecasting: a deep neural networks model based on similar day approach
Gong, Mingju (author) / Zhou, Haojie (author) / Wang, Qilin (author) / Wang, Sheng (author) / Yang, Peng (author)
Advances in Building Energy Research ; 14 ; 372-388
2020-07-02
17 pages
Article (Journal)
Electronic Resource
Unknown
XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks
| BASE | 2021
Forecasting Heat Load for Smart District Heating Systems : A Machine Learning Approach
| BASE | 2014
Applied machine learning: Forecasting heat load in district heating system
| Online Contents | 2016
Load management in district heating systems
| Elsevier | 1988