Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
A systematic literature review of deep learning neural network for time series air quality forecasting
Rapid progress of industrial development, urbanization and traffic has caused air quality
reduction that negatively affects human health and environmental sustainability, especially …
reduction that negatively affects human health and environmental sustainability, especially …
Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation
The use of machine learning techniques in waste management studies is increasingly
popular. Recent literature suggests k-fold cross validation may reduce input dataset partition …
popular. Recent literature suggests k-fold cross validation may reduce input dataset partition …
Wind power forecasting–A data-driven method along with gated recurrent neural network
Effective wind power prediction will facilitate the world's long-term goal in sustainable
development. However, a drawback of wind as an energy source lies in its high variability …
development. However, a drawback of wind as an energy source lies in its high variability …
Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model
The stability operation and real-time control of the integrated energy system with distributed
energy resources determines the higher and higher requirements for the accuracy of solar …
energy resources determines the higher and higher requirements for the accuracy of solar …
Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information
Due to flexible and clean nature, distributed photovoltaic (PV) power plants in micro-grid are
essential for solving energy and environmental problems. However, because of the high …
essential for solving energy and environmental problems. However, because of the high …
[HTML][HTML] Trends and gaps in photovoltaic power forecasting with machine learning
The share of solar energy in the electricity mix increases year after year. Knowing the
production of photovoltaic (PV) power at each instant of time is crucial for its integration into …
production of photovoltaic (PV) power at each instant of time is crucial for its integration into …
LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method
Y Dai, Y Wang, M Leng, X Yang, Q Zhou - Energy, 2022 - Elsevier
Accurate prediction of photovoltaic power generation is vital to guarantee smooth operation
of power stations and ensure users' electricity consumption. As a good forecasting tool …
of power stations and ensure users' electricity consumption. As a good forecasting tool …
A systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score
Electric power forecasting plays a substantial role in the administration and balance of
current power systems. For this reason, accurate predictions of service demands are needed …
current power systems. For this reason, accurate predictions of service demands are needed …
An effective hybrid NARX-LSTM model for point and interval PV power forecasting
This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …