Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction …
J Qu, Z Qian, Y Pei - Energy, 2021 - Elsevier
Accurate forecasting of photovoltaic power plays a pivotal role in the integration, operation,
and scheduling of smart grid systems. Notably, volatility and intermittence of solar energy …
and scheduling of smart grid systems. Notably, volatility and intermittence of solar energy …
Urban vulnerability in the EMME region and sustainable development goals: A new conceptual framework
Crises have shocked the global population and forced entire nations to shift their operations
and priorities. The adverse effects of these crises primarily impact cities and their …
and priorities. The adverse effects of these crises primarily impact cities and their …
Multitask air-quality prediction based on LSTM-autoencoder model
X Xu, M Yoneda - IEEE transactions on cybernetics, 2019 - ieeexplore.ieee.org
With the development of the data-driven modeling techniques, using the neural network to
simulate the transport process of atmospheric pollutants and constructing PM 2.5 time-series …
simulate the transport process of atmospheric pollutants and constructing PM 2.5 time-series …
Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network
Owing to a shortage of fossil fuels, environmental pollution and the greenhouse effect,
renewable energy generation has become important in a modern smart grid. However, the …
renewable energy generation has become important in a modern smart grid. However, the …
An ensemble multi-step M-RMLSSVR model based on VMD and two-group strategy for day-ahead short-term load forecasting
F Yuan, J Che - Knowledge-Based Systems, 2022 - Elsevier
Accurate prediction of the power load is one of the keys to guarantee stable operation of
power construction. However, with the surge of power load, the uncertainty of multi-step …
power construction. However, with the surge of power load, the uncertainty of multi-step …
An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach
Air pollution forecasting is a significant step for air quality pollution management to mitigate
pollution's negative impact on the environment and people's health. The data-driven …
pollution's negative impact on the environment and people's health. The data-driven …
Improving multi-step ahead tourism demand forecasting: A strategy-driven approach
S Sun, Z Du, C Zhang, S Wang - Expert Systems with Applications, 2022 - Elsevier
Previous researches have proposed five strategies to deal with complex multi-step ahead
forecasting tasks. However, these strategies have not received much attention in the field of …
forecasting tasks. However, these strategies have not received much attention in the field of …
Prediction of outdoor PM2. 5 concentrations based on a three-stage hybrid neural network model
H Liu, C Chen - Atmospheric Pollution Research, 2020 - Elsevier
PM 2.5 concentrations forecasting has become an effective method to deal with the severe
air pollution ahead. This study proposes a three-stage hybrid neural network model to …
air pollution ahead. This study proposes a three-stage hybrid neural network model to …
An ensemble multi-step-ahead forecasting system for fine particulate matter in urban areas
In recent years, growing air pollution has become a significant issue due to its detrimental
effects on the environment and different living organisms. Providing accurate and reliable …
effects on the environment and different living organisms. Providing accurate and reliable …
A nested machine learning approach to short-term PM2. 5 prediction in metropolitan areas using PM2. 5 data from different sensor networks
J Li, J Crooks, J Murdock, P de Souza… - Science of The Total …, 2023 - Elsevier
Many predictive models for ambient PM 2.5 concentrations rely on ground observations from
a single monitoring network consisting of sparsely distributed sensors. Integrating data from …
a single monitoring network consisting of sparsely distributed sensors. Integrating data from …