Работы, цитирующие эту работу

Работ: 4

Работа: Predicting residential energy consumption using CNN-LSTM neural networks

1. Advanced techniques for electricity consumption prediction in buildings using comparative correlation analysis, data normalization, and Long Short-Term Memory (LSTM) networks: A case study of a U.S. commercial building

   Moon Keun Kim, Yang‐Seon Kim, Nuodi Fu +4

   СтатьяBuilding Energy and Comfort OptimizationEnergy Reports2025Цитирований: 0

   ABIABI:AkademIndex/openalex/2025.article.000191

2. 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

3. Assessing the efficacy of a sequential general variational mode decomposition-based combination model for United States wind power forecasting

   Mehdi Shalchilar, Rasoul Samei, Sanam Ansari +4

   СтатьяEnergy Load and Power ForecastingModeling Earth Systems and Environment2026Цитирований: 0

   ABIABI:AkademIndex/openalex/2026.article.001521

4. 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