Due to the nonlinear modeling capabilities, deep learning prediction networks have become
widely used for smart agriculture. Because the sensing data has noise and complex …

To achieve accurate medium-and long-term predictions of greenhouse temperature and
humidity, this study proposes a fuzzy adaptive normalized encoding and decoding network …

As the digitalization process has become more and more important in our daily lives, during
recent decades e-commerce has greatly increased in popularity, becoming increasingly …

Accurate short-term load forecasting is crucial for the steady operation of the power system
and power market schedule planning. The extraction of features and training of prediction …

Household power load forecasting plays an important role in the operation and planning of
power grids. To address the prediction issue of household power consumption in power …

For accelerating the technology development and facilitating the reliable operation of lithium-
ion batteries, accurate prediction for battery remaining useful life (RUL) are both critical. In …

This study uses the Scopus and Web of Science databases to review quantitative methods
to forecast electricity consumption from 2015 to 2024. Using the PRISMA approach, 175 …

The paper introduced a deep learning algorithm, convolutional neural network Long Short-
Term Memory (CNN-LSTM), which has yet to be deeply researched for classifying the …

Given the depletion of hydrocarbon reserves, the oil industry is developing enhanced
recovery processes, including waterflooding, to increase the quantity of hydrocarbon to be …

Increased focus on sustainability and energy decentralization has positively impacted the
adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for …