Strategies for microgrid operation under real-world conditions
Microgrids are an increasingly relevant technology for integrating renewable energy sources
into electricity systems. Based on a microgrid implementation in California, we investigate …
into electricity systems. Based on a microgrid implementation in California, we investigate …
Survey on renewable energy forecasting using different techniques
VA Natarajan, P Karatampati - 2019 2nd International …, 2019 - ieeexplore.ieee.org
Wind and solar are the renewable technologies which are very popular and well known
source of energies throughout the world. Fossil fuels are formed by natural processes which …
source of energies throughout the world. Fossil fuels are formed by natural processes which …
A deep learning approach to solar-irradiance forecasting in sky-videos
TA Siddiqui, S Bharadwaj… - 2019 IEEE winter …, 2019 - ieeexplore.ieee.org
Ahead-of-time forecasting of incident solar-irradiance on a panel is indicative of expected
energy yield and is essential for efficient grid distribution and planning. Traditionally, these …
energy yield and is essential for efficient grid distribution and planning. Traditionally, these …
A machine-learning approach combining wavelet packet denoising with catboost for weather forecasting
D Niu, L Diao, Z Zang, H Che, T Zhang, X Chen - Atmosphere, 2021 - mdpi.com
Accurate forecasting of future meteorological elements is critical and has profoundly affected
human life in many aspects from rainstorm warning to flight safety. The conventional …
human life in many aspects from rainstorm warning to flight safety. The conventional …
Weather forecasting using ensemble of spatial-temporal attention network and multi-layer perceptron
Y Li, J Lang, L Ji, J Zhong, Z Wang, Y Guo… - Asia-Pacific Journal of …, 2021 - Springer
Weather forecasting is a challenging task, which is especially suited for artificial intelligence
due to the large amount of data involved. This paper proposed an end-to-end hybrid …
due to the large amount of data involved. This paper proposed an end-to-end hybrid …
A vector autoregression weather model for electricity supply and demand modeling
Weather forecasting is crucial to both the demand and supply sides of electricity systems.
Temperature has a great effect on the demand side. Moreover, solar and wind are very …
Temperature has a great effect on the demand side. Moreover, solar and wind are very …
Multi-step short-term wind speed prediction using a residual dilated causal convolutional network with nonlinear attention
K Shivam, JC Tzou, SC Wu - Energies, 2020 - mdpi.com
Wind energy is the most used renewable energy worldwide second only to hydropower.
However, the stochastic nature of wind speed makes it harder for wind farms to manage the …
However, the stochastic nature of wind speed makes it harder for wind farms to manage the …
An imbalanced data handling framework for industrial big data using a gaussian process regression-based generative adversarial network
The developments in the fields of industrial Internet of Things (IIoT) and big data
technologies have made it possible to collect a lot of meaningful industrial process and …
technologies have made it possible to collect a lot of meaningful industrial process and …
Short term power load prediction with knowledge transfer
Y Zhang, G Luo - Information Systems, 2015 - Elsevier
A novel transfer learning method is proposed in this paper to solve the power load forecast
problems in the smart grid. Prediction errors of the target tasks can be greatly reduced by …
problems in the smart grid. Prediction errors of the target tasks can be greatly reduced by …
Spatiotemporal prediction of infectious diseases using structured Gaussian processes with application to Crimean–Congo hemorrhagic fever
Background Infectious diseases are one of the primary healthcare problems worldwide,
leading to millions of deaths annually. To develop effective control and prevention …
leading to millions of deaths annually. To develop effective control and prevention …