# Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Machine Learning-Based Forecasting of Renewable Energy

#### 2.1. Supervised Learning

^{2}of 0.97, and a MAE of 32.57, in contrast to RFR’s RMSE of 86, an adjusted R

^{2}of 0.90, and a MAE of 69 [62].

#### 2.2. Unsupervised Learning

#### 2.3. Reinforcement Learning Algorithms

#### 2.4. Deep Learning (DL)

## 3. DL Algorithms Used for Renewable Energy Forecasting

#### 3.1. ANN for Renewable Energy Forecasting

#### 3.2. CNN for Renewable Energy Forecasting

^{2}and demonstrating stable performance in different climates [126]. Cannizzaro et al. (2021) presented a fresh approach to anticipating short- and long-term global horizontal solar irradiance (GHI) through machine learning techniques. Their methodology involves a combination of variational mode decomposition (VMD) and CNN with either RF or LSTM. The approach is evaluated on a real-world dataset and achieves accurate results [127]. Furthermore, Wu et al. (2020) suggest a spatio-temporal correlation model (STCM) that utilizes CNN-LSTM to forecast ultra-short-term wind power. The model reconstructs meteorological factors at different sites from input data using CNN to extract spatial correlation features and LSTM to extract temporal correlation features. The STCM performs better than traditional models and accurately forecasts wind power using measured meteorological factors and wind power datasets from a wind farm in China [128].

#### 3.3. RNN for Renewable Energy Forecasting

#### 3.4. RBM for Renewable Energy Forecasting

#### 3.5. Auto Encoder for Renewable Energy Forecasting

#### 3.6. Deep Belief Neural Networks (DBN) for Renewable Energy Forecasting

#### 3.7. ANFIS for Renewable Energy Forecasting

#### 3.8. Wavelet Neural Network (WNN) for Renewable Energy Forecasting

#### 3.9. RBNN for Renewable Energy Forecasting

#### 3.10. GRNN for Renewable Energy Forecasting

#### 3.11. ELM for Renewable Energy Forecasting

#### 3.12. Ensemble Learning for Renewable Energy Forecasting

#### 3.13. Transfer Learning (TL) for Renewable Energy Forecasting

#### 3.14. Hybrid Model (HM) for Forecasting Renewable Energy

## 4. Challenges and Future Prospects

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**Convolutional neural network architecture [120].

**Figure 5.**RBM forecasting architecture [138].

**Figure 6.**The autoencoder’s basic design [145].

**Figure 7.**Architecture of DBN [138].

**Figure 8.**ANFIS architecture [156].

**Figure 9.**The schematic representation of GRNN [177].

Technique | Pros | Cons | Applications |
---|---|---|---|

Linear Regression | Easy to implement, fast training | Limited to linear relationships | Predictive analytics |

Logistic Regression | Interpretable, works well with small datasets | Assumes linearity, apply only for classification | Predict power outages, classify extreme weather events, market, and healthcare |

Decision Trees | Interpretable, can handle both categorical and continuous data | Prone to overfitting | Predictive maintenance, finance |

Random Forest | High accuracy, less prone to overfitting | Computationally expensive compared to DT, difficult to interpret | Operation control strategy, image classification, and fraud detection |

SVM | Can handle high-dimensional data, can handle non-linear relationships, robust to noise | Computationally expensive and requires careful parameter tuning | Text classification, bioinformatics |

K-means clustering | Simple and fast, useful for data exploration and segmentation | Requires a pre-determined number of clusters and can be sensitive to initial conditions | Market segmentation, image segmentation |

PCA | Can reduce dimensionality and noise in data, useful for data exploration and visualization | May not capture all relevant information and can be difficult to interpret | Image and speech recognition, natural language processing |

Reinforcement Learning | Can learn through trial and error, useful for decision-making in dynamic environments | Requires a lot of data and can be prone to overfitting | Game playing, robotics |

ANN | Can learn complex relationships, handle large datasets, and model non-linear relationships | Requires large amounts of data and can be difficult to interpret | Predict energy demand (stationary), energy resource forecasting, image recognition, and speech recognition |

CNN | Highly effective for image analysis, it can learn features automatically | Requires large amounts of data, is computationally expensive, may not be suitable for low spatial or temporal resolutions | Object detection, image classification, and predicting energy demand based on satellite images of areas |

RNN | Can handle sequential data and time series data and can handle long-term dependencies | Can be prone to overfitting and slow training, and may suffer from vanishing or exploding gradients | Energy price forecasting (time series), speech recognition, and sentiment analysis |

LSTM | Can handle long-term dependencies, which is useful for time series data | Can be prone to overfitting and require careful tuning | Time series, speech recognition, natural language processing, load forecasting, and energy price forecasting (time series) |

Autoencoders | Can reduce dimensionality and noise in data and be used for unsupervised learning | Requires large amounts of data and can be difficult to interpret | Anomaly detection, image, and speech recognition |

ELM | Fast training, can handle large datasets | Limited interpretability may not generalize well to new data | Renewable energy forecasting, image and speech recognition, predictive analytics |

GRNN | Fast training and can handle noise in data | Limited to regression tasks and may not scale well to large datasets | Renewable energy forecasting, time series prediction, and function approximation |

RBNN | Effective for non-linear regression and classification tasks | Requires careful tuning of network architecture and hyperparameters | Image and speech recognition, anomaly detection |

WNN | Can handle multi-resolution and multi-scale data | Requires careful selection of wavelet basis functions and can be computationally expensive | Image and signal processing, time series prediction |

ANFIS | Can handle uncertainty and non-linearity in data | Requires careful selection and tuning of fuzzy rules and can be computationally expensive | Control systems, fault diagnosis |

DBN | Can learn hierarchical representations of data, which is effective for unsupervised learning | Requires large amounts of data, can be difficult to interpret | Image and speech recognition, natural language processing |

Ensemble Learning | Can improve performance and reduce overfitting by combining multiple models | Can be computationally expensive and may require careful tuning | Renewable energy forecasting, image and speech recognition, and natural language processing |

Transfer Learning | Can leverage pre-trained models to improve performance and require less data | May not generalize well to new data, limited to similar tasks | Load forecasting, energy price prediction, predictive maintenance, fault diagnosis, energy consumption, energy efficiency forecasting, renewable energy foresting, image and speech recognition, and natural language processing |

Algorithms Used | Application | Inputs Used | Prediction Outputs | Ref. |
---|---|---|---|---|

ANN and regression models (LR, M5P, DT, and Gaussian process regression (GPR)) | Solar Energy | Solar irradiance, ambient temperature, relative humidity, PV surface temperature, wind speed, and dust on PV panels. | The hourly power output of the PV system | [209] |

MARS, CART, M5, and random forest | Solar Energy | Minimum, maximum, and average temperature, wind speed, rainfall, dew point, GSR, atmospheric pressure, and solar azimuth | One to six days’ worth of hourly solar radiation | [22] |

ANN | Solar Energy | Pressure, relative humidity, wind speed, ambient temperature, and sunshine duration | Monthly average daily GSR | [20] |

ANN, DBN, autoencoder, and LSTM | Solar Energy | Sunshine hours, daily average solar irradiation, location, temperature, etc. | Solar power | [210] |

SVM, ANN, DL, kNN | Solar Energy | Local time, temperature, pressure, wind speed, relative humidity, and past time-series solar radiation | Hourly solar radiation | [23] |

ANN | Solar Energy | Global solar irradiance, direct beam solar irradiance, time, and power generated from the solar PV | Forecasting solar power | [211] |

Deep CNN | Solar Energy | Longitude, latitude, time, and altitude; humidity, temperature, wind velocity, moisture, etc. | Solar power predictions | [212] |

ANN, kernel ELM | Wind Energy | Wind speed | Short-term wind speed forecasting | [30] |

BP network, RBF network, and NARX models | Wind Energy | Time series historical weather data for three years in 15 min intervals: Wind direction and speed, radiation, temperature, reflected radiation, humidity, etc. | Prediction of wind speed | [213] |

2D-CNN | Wind Energy | Historical wind speeds | Twenty-four-hour forecasting of wind speed | [15] |

LSSVM, HM, LMD | Wind Energy | Five short-term wind speed datasets | Short-term wind speed prediction | [33] |

LASSO, kNN, RF, XGBoost, SVR | Wind Energy | Daily wind speed, daily standard deviation, and daily wind power | Long-term wind power forecasting | [29] |

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## Share and Cite

**MDPI and ACS Style**

Benti, N.E.; Chaka, M.D.; Semie, A.G.
Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects. *Sustainability* **2023**, *15*, 7087.
https://doi.org/10.3390/su15097087

**AMA Style**

Benti NE, Chaka MD, Semie AG.
Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects. *Sustainability*. 2023; 15(9):7087.
https://doi.org/10.3390/su15097087

**Chicago/Turabian Style**

Benti, Natei Ermias, Mesfin Diro Chaka, and Addisu Gezahegn Semie.
2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects" *Sustainability* 15, no. 9: 7087.
https://doi.org/10.3390/su15097087