Работы, цитирующие эту работу

Работ: 2

Работа: ISSA-LSTM: A new data-driven method of heat load forecasting for building air conditioning

1. Electricity Demand Forecasting for Cultural Institutions: A Comparative Study of Lstm and Cnn-Lstm Models with Three Data Normalization Techniques Using Weather and Price Data – Case Studies from Norwegian Museums

   Nuodi Fu, Jaehee Lee, Jiying Liu +2

   ПрепринтConservation Techniques and StudiesSSRN Electronic Journal2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.preprint.000135

2. Dynamic Energy Demand Forecasting in a Norwegian Commercial Store Using Local Building Time-Series Data: CNN-LSTM and Transformer Architectures Compared

   이재희, Nuodi Fu, Moon Keun Kim +3

   ПрепринтEnergy Load and Power ForecastingSSRN Electronic Journal2026Цитирований: 0

   ABIABI:AkademIndex/openalex/2026.preprint.000113