A holistic review on energy forecasting using big data and deep learning models
J Devaraj, R Madurai Elavarasan… - … journal of energy …, 2021 - Wiley Online Library
With the growth of forecasting models, energy forecasting is used for better planning,
operation, and management in the electric grid. It is important to improve the accuracy of …
operation, and management in the electric grid. It is important to improve the accuracy of …
[HTML][HTML] Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?
J Devaraj, RM Elavarasan, R Pugazhendhi… - Results in Physics, 2021 - Elsevier
The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global
economic growth and henceforth, all of society since neither a curing drug nor a preventing …
economic growth and henceforth, all of society since neither a curing drug nor a preventing …
Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning
Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG)
systems involving various applications such as demand‐side management, load shedding …
systems involving various applications such as demand‐side management, load shedding …
Deep-learning-based short-term electricity load forecasting: A real case application
The rising popularity of deep learning can largely be attributed to the big data phenomenon,
the surge in the development of new and novel deep neural network architectures, and the …
the surge in the development of new and novel deep neural network architectures, and the …
In-process tool condition forecasting based on a deep learning method
It is widely acknowledged that machining precision and surface integrity are greatly affected
by cutting tool conditions. In order to enable early cutting tool replacement and proactive …
by cutting tool conditions. In order to enable early cutting tool replacement and proactive …
Toward multi-label sentiment analysis: a transfer learning based approach
Sentiment analysis is recognized as one of the most important sub-areas in Natural
Language Processing (NLP) research, where understanding implicit or explicit sentiments …
Language Processing (NLP) research, where understanding implicit or explicit sentiments …
Multi-site household waste generation forecasting using a deep learning approach
M Cubillos - Waste Management, 2020 - Elsevier
Forecasting household waste generation using traditional methods is particularly
challenging due to its high variability and uncertainty. Unlike studies that forecast waste …
challenging due to its high variability and uncertainty. Unlike studies that forecast waste …
Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network
Accurate prediction of domestic waste generation is a challenging task for municipalities to
implement sustainable waste management strategies. In the present study, domestic waste …
implement sustainable waste management strategies. In the present study, domestic waste …
[HTML][HTML] Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction
DG da Silva, AA de Moura Meneses - Energy Reports, 2023 - Elsevier
Electric consumption prediction methods are investigated for many reasons, such as
decision-making related to energy efficiency as well as for anticipating demand and the …
decision-making related to energy efficiency as well as for anticipating demand and the …
Intelligent forecasting model of stock price using neighborhood rough set and multivariate empirical mode decomposition
J Bai, J Guo, B Sun, Y Guo, Q Bao, X Xiao - Engineering Applications of …, 2023 - Elsevier
Intelligent forecasting model of stock price is an effective way to obtain ideal investment
returns. Due to the impact of quantitative transactions, traditional forecasting methods face …
returns. Due to the impact of quantitative transactions, traditional forecasting methods face …