Работы, цитирующие эту работу

Работ: 4

Работа: Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) Network Models for Forecasting Energy Consumptions

1. Forecasting electricity consumption by LSTM neural network

   V. Ya. Ushakov, Ikromjon U. Rakhmonov, Numon N. Niyozov +1

   СтатьяEnergy Load and Power ForecastingBulletin of the Tomsk Polytechnic University Geo Assets Engineering2023Цитирований: 24

   ABIABI:AkademIndex/openalex/2023.article.000225

2. Energy saving potential in lighting and basic approaches to its implementation

   Abdulkhay Rasulov, Maqsud Kurbanbayev, Davron Arziyev +1

   СтатьяEngineering and Agricultural InnovationsAIP conference proceedings2024Цитирований: 11

   ABIABI:AkademIndex/openalex/2024.article.000690

3. Energy efficiency indicators

   I U Rakhmonov, Zamira Shayumova, Kamal Reymov +1

   СтатьяEnergy Load and Power ForecastingAIP conference proceedings2024Цитирований: 10

   ABIABI:AkademIndex/openalex/2024.article.000565

4. Stimulating environmental protection activities in the energy sector

   Sultonkhoja Makhmutkhanov, Jumayev Akhrom, Shukhrat Djurayev +1

   СтатьяEngineering and Agricultural InnovationsAIP conference proceedings2024Цитирований: 8

   ABIABI:AkademIndex/openalex/2024.article.000873