Feature Selection and ANN Solar Power Prediction
A novel method of solar power forecasting for individuals and small businesses is developed in this paper based on machine learning, image processing, and acoustic classification techniques. Increases in the production of solar power at the consumer level require automated forecasting systems to min...
Main Authors: | Daniel O’Leary, Joel Kubby |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2017-01-01
|
Series: | Journal of Renewable Energy |
Online Access: | http://dx.doi.org/10.1155/2017/2437387 |
Similar Items
-
The Effectiveness of Feature Selection Method in Solar Power Prediction
by: Md Rahat Hossain, et al.
Published: (2013-01-01) -
Prediction of the biogas production using GA and ACO input features selection method for ANN model
by: Tanja Beltramo, et al.
Published: (2019-09-01) -
Gender, ethnic and institutional features of the UK labour market : an investigation using decomposition analysis
by: O'Leary, C.
Published: (1999) -
Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models
by: Junior, O.H.A, et al.
Published: (2022) -
Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction
by: Si Chen, et al.
Published: (2020-11-01)