Application of big data and machine learning in smart grid, and associated security concerns: A review
This paper conducts a comprehensive study on the application of big data and machine
learning in the electrical power grid introduced through the emergence of the next …
learning in the electrical power grid introduced through the emergence of the next …
Wind power day-ahead prediction with cluster analysis of NWP
L Dong, L Wang, SF Khahro, S Gao, X Liao - Renewable and Sustainable …, 2016 - Elsevier
The selection of training data for establishing a model directly affects the prediction
precision. Wind power has the characteristic of daily similarity. The corresponding …
precision. Wind power has the characteristic of daily similarity. The corresponding …
Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis
Wind power is a clean resource that is widely used as a renewable energy source. Accurate
wind power forecasting is important for the efficient and stable use of wind energy. The …
wind power forecasting is important for the efficient and stable use of wind energy. The …
Mlaas: Machine learning as a service
M Ribeiro, K Grolinger… - 2015 IEEE 14th …, 2015 - ieeexplore.ieee.org
The demand for knowledge extraction has been increasing. With the growing amount of data
being generated by global data sources (eg, social media and mobile apps) and the …
being generated by global data sources (eg, social media and mobile apps) and the …
One dimensional convolutional neural network architectures for wind prediction
S Harbola, V Coors - Energy Conversion and Management, 2019 - Elsevier
This paper proposes two one-dimensional (1D) convolutional neural networks (CNNs) for
predicting dominant wind speed and direction for the temporal wind dataset. The proposed …
predicting dominant wind speed and direction for the temporal wind dataset. The proposed …
Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks
H Liu, H Tian, X Liang, Y Li - Applied Energy, 2015 - Elsevier
Wind speed forecasting technology is important in the field of wind power. However, the
wind speed signals are always nonlinear and non-stationary so that it is difficult to predict …
wind speed signals are always nonlinear and non-stationary so that it is difficult to predict …
Short-term wind power prediction based on LSSVM–GSA model
X Yuan, C Chen, Y Yuan, Y Huang, Q Tan - Energy Conversion and …, 2015 - Elsevier
Wind power forecasting can improve the economical and technical integration of wind
energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to …
energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to …
Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression
GF Fan, LL Peng, WC Hong, F Sun - Neurocomputing, 2016 - Elsevier
Electric load forecasting is an important issue for power utility, associated with the
management of daily operations such as energy transfer scheduling, unit commitment, and …
management of daily operations such as energy transfer scheduling, unit commitment, and …
A Gaussian process regression based hybrid approach for short-term wind speed prediction
C Zhang, H Wei, X Zhao, T Liu, K Zhang - Energy conversion and …, 2016 - Elsevier
This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian
process regression (GPR) for probabilistic wind speed forecasting. In the proposed …
process regression (GPR) for probabilistic wind speed forecasting. In the proposed …
Deep learning-based multistep ahead wind speed and power generation forecasting using direct method
Long-term effective and accurate wind power potential prediction, especially for wind farms,
facilitates planning for the sustainable development of renewable energy. Accurate wind …
facilitates planning for the sustainable development of renewable energy. Accurate wind …