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Machine Learning Applications for Renewable-Based Energy Systems

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Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Machine learning is becoming a fundamental tool in current energy systems. It helps to obtain accurate predictions of the variable renewable energy (VRE) generation, energy demand, or possible network outages, conferring to power system operators the possibility to make the needed actions to balance load and generation in intraday and day-ahead scheduling with benefits for operational costs, environmental impact, and system reliability. If the short-term forecasting is fundamental for operational aspects, the long-term forecasting is crucial for system planning. Moreover, an improvement in the granularity of the forecasting can help to go towards real-time solutions. The machine learning can be employed effectively in all these contexts. This chapter deals with a critical analysis of machine learning methods and techniques for renewable-based energy systems showing the advantages of introducing these novel approaches in future energetic scenarios through discussing some relevant case studies.

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Notes

  1. 1.

    In this context, reference is made to the concept of the confidence estimate of the predictive models. Recently the term trustworthy ML is used to indicate ML models that are explainable, fair, robust, causal and that preserve privacy.

  2. 2.

    https://dvc.org/

  3. 3.

    https://neptune.ai/

  4. 4.

    https://wandb.ai/

Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

ARIMA:

Autoregressive integrated moving average

CART:

Classification and Regression Tree

CNN:

Convolutional neural network

DER:

Distributed energy resources

DL:

Deep learning

FCM:

Fuzzy C-Means

FCNN:

Fully connected neural network

FFNN:

Feed forward neural network

FL:

Fuzzy logic

GAN:

Generative adversarial network

GRU:

Gated recurrent unit

HME:

Hierarchical mixture of experts

HVAC:

Heating, ventilation, and air conditioning

LightGBM:

Light Gradient Boosting Machine

LSTM:

Long short-term memory

LTLF:

Long-term load forecast

ML:

Machine learning

MLP:

Multilayer perceptron

MTLF:

Medium-term load forecast

N-BEATS:

Neural basis expansion analysis for interpretable time series

NWP:

Numerical weather prediction

PV:

Photovoltaic

RES:

Renewable energy sources

RNN:

Recurrent neural network

STLF:

Short-term load forecast

SVM:

Support vector machine

SVR:

Support Vector Regression

TCN:

Temporal convolutional network

XGBoost:

eXtreme Gradient Boosting

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    Giorgio Graditi, Amedeo Buonanno, Martina Caliano, Marialaura Di Somma & Maria Valenti

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  3. Institute of Applied Mathematics, Middle East Technical University, Ankara, Türkiye

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  4. Ton Duc Thang University, Ho Chi Minh City, Vietnam

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Graditi, G., Buonanno, A., Caliano, M., Di Somma, M., Valenti, M. (2023). Machine Learning Applications for Renewable-Based Energy Systems. In: Manshahia, M.S., Kharchenko, V., Weber, GW., Vasant, P. (eds) Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26496-2_9

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