Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning

Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads

(2020) X.J. Luo et al.   Sustainable Cities and Society

A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network

(2019) Kejun Wang et al.   APPLIED ENERGY

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

(2018) Xiangyun Qing et al.   ENERGY

LSTM with sentence representations for document-level sentiment classification

(2018) Guozheng Rao et al.   NEUROCOMPUTING

Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

(2017) Bingchun Liu et al.   Energies

An adaption scheduling based on dynamic weighted random forests for load demand forecasting

(2017) Mincheng Chen et al.   JOURNAL OF SUPERCOMPUTING

LSTM: A Search Space Odyssey

(2017) Klaus Greff et al.   IEEE Transactions on Neural Networks and Learning Systems

Deep Learning for Household Load Forecasting – A Novel Pooling Deep RNN

(2017) Heng Shi et al.   IEEE Transactions on Smart Grid

Short-Term Residential Load Forecasting based on LSTM Recurrent Neural Network

(2017) Weicong Kong et al.   IEEE Transactions on Smart Grid

Deep belief network based deterministic and probabilistic wind speed forecasting approach

(2016) H.Z. Wang et al.   APPLIED ENERGY

Deep Neural Network Based Demand Side Short Term Load Forecasting

(2016) Seunghyoung Ryu et al.   Energies

Deep belief network based electricity load forecasting: An analysis of Macedonian case

(2016) Aleksandra Dedinec et al.   ENERGY

Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

(2016) Shouxiang Wang et al.   RENEWABLE ENERGY

Performance of state space and ARIMA models for consumer retail sales forecasting

(2015) Patrícia Ramos et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

From Feedforward to Recurrent LSTM Neural Networks for Language Modeling

(2015) Martin Sundermeyer et al.   IEEE-ACM Transactions on Audio Speech and Language Processing