scholarly journals A Wind Energy Prediction Scheme Combining Cauchy Variation and Reverse Learning Strategy

2021 ◽  
Vol 21 (4) ◽  
pp. 3-10
Author(s):  
X. WU ◽  
X. SHEN ◽  
J. ZHANG ◽  
Y. ZHANG
Keyword(s):  
Wind Energy ◽  
Learning Strategy ◽  
Energy Prediction ◽  
Prediction Scheme

scholarly journals Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy

2020 ◽  
Vol 10 (23) ◽  
pp. 8634
Author(s):  
Zulfiqar Ahmad Khan ◽  
Amin Ullah ◽  
Waseem Ullah ◽  
Seungmin Rho ◽  
Miyoung Lee ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Learning Strategy ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Management Policy ◽  
Mean Square ◽  
Load Prediction ◽  
Energy Prediction ◽  
Proposed Model

Smart grid technology based on renewable energy and energy storage systems are attracting considerable attention towards energy crises. Accurate and reliable model for electricity prediction is considered a key factor for a suitable energy management policy. Currently, electricity consumption is rapidly increasing due to the rise in human population and technology development. Therefore, in this study, we established a two-step methodology for residential building load prediction, which comprises two stages: in the first stage, the raw data of electricity consumption are refined for effective training; and the second step includes a hybrid model with the integration of convolutional neural network (CNN) and multilayer bidirectional gated recurrent unit (MB-GRU). The CNN layers are incorporated into the model as a feature extractor, while MB-GRU learns the sequences between electricity consumption data. The proposed model is evaluated using the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. Finally, our model is assessed over benchmark datasets that exhibited an extensive drop in the error rate in comparison to other techniques. The results indicated that the proposed model reduced errors over the individual household electricity consumption prediction (IHEPC) dataset (i.e., RMSE (5%), MSE (4%), and MAE (4%)), and for the appliances load prediction (AEP) dataset (i.e., RMSE (2%), and MAE (1%)).

Wind energy prediction and monitoring with neural computation

2013 ◽  
Vol 109 ◽  
pp. 84-93 ◽  
Author(s):  
Oliver Kramer ◽  
Fabian Gieseke ◽  
Benjamin Satzger
Keyword(s):  
Wind Energy ◽  
Neural Computation ◽  
Energy Prediction

scholarly journals Multitask Support Vector Regression for Solar and Wind Energy Prediction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6308
Author(s):  
Carlos Ruiz ◽  
Carlos M. Alaíz ◽  
José R. Dorronsoro
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Support Vector Regression ◽  
Energy Production ◽  
Multitask Learning ◽  
Support Vector ◽  
Prediction Problem ◽  
Energy Prediction ◽  
The Common ◽  
The Impact

Given the impact of renewable sources in the overall energy production, accurate predictions are becoming essential, with machine learning becoming a very important tool in this context. In many situations, the prediction problem can be divided into several tasks, more or less related between them but each with its own particularities. Multitask learning (MTL) aims to exploit this structure, training several models at the same time to improve on the results achievable either by a common model or by task-specific models. In this paper, we show how an MTL approach based on support vector regression can be applied to the prediction of photovoltaic and wind energy, problems where tasks can be defined according to different criteria. As shown experimentally with three different datasets, the MTL approach clearly outperforms the results of the common and specific models for photovoltaic energy, and are at the very least quite competitive for wind energy.

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