# SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

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## Abstract

**:**

## 1. Introduction

_{2}emissions [2]. Developing capacity for solar energy production is also critical for Saudi Arabia, which is among the top few oil producers and consumers in the world and is ranked sixth in the world in terms of its potential for producing solar energy [3]. The Sakaka 300-megawatt (MW) solar power station, Saudi Arabia’s first utility-scale solar PV project, was linked to the national grid in November 2019. With a $302 million investment, the plant will cover a six square kilometer area in Al-Jouf. This is the first in a series of projects under Saudi Arabia’s national renewable energy program to generate 9.5 GW of renewable energy by 2023 [4].

- This paper proposes a novel approach and tool that uses deep learning to automatically predict the best-performing solar energy forecasting model. The approach is extensible to other performance metrics or user preferences and is applicable to other energy sources and problems.
- We provide an in-depth analysis of five deep learning models for solar energy forecasting using ten datasets from three continents. This is the first time that such a combination of models, datasets, and analyses has been reported. Particularly, none of the earlier works have reported forecasting based on five deep learning-based models with such many locations in Saudi Arabia and provided a comparison with locations abroad (Toronto and Caracas).
- We highlight the need for standardization in performance evaluation of machine and deep learning modelling in solar forecasting by providing extensive analysis and visualization of the tool and its comparison with other works using several performance metrics. We have not seen such an extensive evaluation of work earlier in solar energy forecasting. This paper is expected to open new avenues for higher depth and transparency in benchmarking of solar energy forecasting methods.

## 2. Literature Review

#### Research Gap

## 3. SENERGY: Methodology and Design

#### 3.1. Tool Development Process

#### 3.2. Datasets Development

#### 3.2.1. Data Collection

#### 3.2.2. Datasets for Forecasting

- GHI: the total amount of shortwave radiation received from sun by a surface horizontal to the ground. It is calculated using the following equation, which explains how GHI is related to DHI, DNI, and the Zenith Angle (ZA) [52];

- 2.
- DHI: solar radiation that does not come on a direct path from the sun, but has been spread by particles and molecules in the atmosphere and comes equally from all directions;
- 3.
- DNI: solar radiation that comes in a straight path from the direction of the sun at its current place in the sky. On a sunny day, GHI consists of 20% DHI and 80% DNI [52].
- 4.
- ZA: the angle between the sun’s rays and a vertical line;
- 5.
- 6.
- Wind speed (WS) and wind direction (WD) at 3 m;
- 7.
- Barometric pressure (BP);
- 8.

#### 3.2.3. Datasets for Model Prediction

#### 3.3. Feature Importance

#### 3.3.1. Pearson’s Correlation

#### 3.3.2. Mutual Information

#### 3.3.3. Forward Feature Selection (FFS) and Backward Feature Elimination (BFE)

#### 3.3.4. LASSO Feature Selection

#### 3.4. Models’ Development

#### 3.4.1. Long Short-Term Memory (LSTM)

#### 3.4.2. Gated Recurrent Unit (GRU)

#### 3.4.3. Convolutional Neural Network (CNN)

#### 3.4.4. Hybrid CNN-Bidirectional LSTM (CNN-BiLSTM)

#### 3.4.5. LSTM Autoencoder (LSTM-AE)

#### 3.5. Performance Evaluation Metrics

^{2}) is a statistical measure that defines the amount of variance in the dependent variable that can be explained by the independent variable. It displays the data’s fit to the regression model. R

^{2}value ranges from 0 to 1 and a higher coefficient indicates a better fit for the model. It is calculated as follows [75].

#### 3.6. Tool Implementation

## 4. SENERGY: Results and Evaluation

#### 4.1. SENERGY: Forecasting Engine Performance

#### 4.1.1. Effect of Lagged Features on Forecasting

#### 4.1.2. Effect of Climate and Location on Forecasting

#### 4.1.3. Effect of Sunny and Cloudy Weather on Forecasting

#### 4.1.4. Effect of Summer and Winter Seasons on Forecasting

#### 4.1.5. Digging Deeper into Forecasting Error for Each GHI Prediction

#### 4.2. SENERGY: Auto-Selective Model Prediction Engine Performance

#### 4.2.1. Model Prediction: Climate and Location

#### 4.2.2. Model Prediction: Sunny and Cloudy Weathers

#### 4.2.3. Model Prediction: Summer and Winter Seasons

#### 4.3. SENERGY: Performance Gain and Loss

#### 4.3.1. Actual Gains and Losses

#### 4.3.2. Potential Performance

#### 4.4. SENERGY: Comparison with Other Works

_{MAE}and FS

_{RMSE}. Equations of all these metrics are provided in Section 3.5. Moreover, GHI mean and standard deviation are added to the comparison to show the variation among locations.

_{MAE}results of Gao et al. [24] are compared to the five forecasting models and the SENERGY results in this work. In this figure, the highest value is the best. It can be seen that the LSTM-AE model and SENERGY have the highest value and the lowest variation between locations, whereas the CNN model is the worst in terms of value and the CNN-BiLSTM model is the worst in terms of variation among locations. In (b), the FS

_{RMSE}results of three works, Gao et al. [24], Fouilloy et al. [25], and Bouzgou and Gueymard [30], are compared to the five forecasting models and SENERGY results in this work. Again, the LSTM-AE model and SENERGY have the highest value and the lowest variation between locations. The second best performance is achieved by the work of Gao et al. [24]. However, they only include the results of four locations, compared to ten and twenty in other works. On the other hand, Bouzgou and Gueymard [30] has the worst value and the largest variation between locations, since it includes twenty results. As in the MAE and RMSE results, SENERGY does not show better performance than the LSTM-AE model in (a) or (b) because on both metrics, the latter is the best model for all locations (refer to Figure 26).

#### 4.5. Results Summary

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

NWP | Numerical Weather Prediction |

RNN | Recurrent Neural Network |

ANN | Artificial neural network |

AE | Autoencoder |

LSTM | Long Short-Term Memory |

GRU | Gated Recurrent Unit |

MI | Mutual Information |

MLP | Multilayer Perceptron Network |

ARMA | Autoregressive Moving Average |

WT | Wavelet Transform |

CEEMDAN | Complete Ensemble Empirical Mode Decomposition with Adaptive Noise |

SVM | Support Vector Machine |

RMSE | Root Mean Square Error |

nRMSE | Normalized Root Mean Square Error |

MAPE | Mean Absolute Percentage Error |

nMAPE | Normalized Mean Absolute Percentage Error |

MAE | Mean Absolute Error |

nMAE | normalized Mean Absolute Error |

MSE | Mean Squared Error loss |

WS | Wind Speed |

AT | Air Temperature |

RH | Relative Humidity |

ML | Machine Learning |

CNN | Convolutional Neural Network |

PV | Photovoltaic |

AI | Artificial Intelligence |

SVR | Support Vector machine Regression |

FFNN | Feed Forward Neural Network |

GHI | Global Horizontal Irradiation |

RF | Random Forest |

DNN | Deep Neural Network |

ELM | Extreme Learning Machine |

BPNN | Back Propagation Neural Network |

ReLU | Rectified Linear Unit |

DHI | Diffuse Horizontal Irradiation |

DNI | Direct Normal Irradiance |

BiLSTM | Bidirectional LSTM |

FS | Forecast Skill |

XGBoost | eXtreme Gradient Boosting |

ACF | Autocorrelation Function |

PACF | Partial Autocorrelation Function |

WD | Wind Direction |

BP | Barometric Pressure |

ZA | Zenith Angle |

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**Figure 7.**The relationship between GHI and the meteorological variables in: (

**a**) Al-Baha; (

**b**) Al-Jouf; and (

**c**) Hail.

**Figure 8.**(

**a**) Autocorrelation function. (

**b**) Partial autocorrelation function of GHI and its lagged readings.

**Figure 9.**(

**a**) Percentage of hours (sunny Vs. cloudy) of 10 datasets; (

**b**) GHI (mean and SD) of 10 datasets.

**Figure 15.**MI values of all features for Al-Jouf, Al-Khafji, Wadi-Addwasir, Caracas, and Toronto datasets.

**Figure 16.**Selected features based on the LASSO method: (

**a**) Al-Jouf; (

**b**) Al-Khafji; (

**c**) Caracas; (

**d**) Toronto.

**Figure 27.**Sunny vs. Cloudy—actual Vs. predicted GHI of 5 models for: (

**a**) Al-Jouf sunny; (

**b**) Al-Jouf cloudy; (

**c**) Al-Khafji sunny; (

**d**) Al-Khafji cloudy; (

**e**) Wadi-Addwasir sunny; (

**f**) Wadi-Addwasir cloudy; (

**g**) Caracas sunny; (

**h**) Caracas cloudy; (

**i**) Toronto sunny; (

**j**) Toronto cloudy.

**Figure 28.**Summer vs. winter—actual vs. predicted GHI of 5 models for: (

**a**) Al-Jouf Jan; (

**b**) Al-Jouf Aug; (

**c**) Al-Khafji Jan; (

**d**) Al-Khafji Aug; (

**e**) Wadi-Addwasir Jan; (

**f**) Wadi-Addwasir Aug; (

**g**) Caracas Jan; (

**h**) Caracas Aug; (

**i**) Toronto Jan; (

**j**) Toronto Aug.

**Figure 29.**MAE of summer vs. winter for 5 models for: (

**a**) Al-Baha; (

**b**) Al-Jouf; (

**c**) Al-Khafji; (

**d**) Arar; (

**e**) Hail; (

**f**) Tabuk; (

**g**) Taif; (

**h**) Wadi-Addwasir; (

**i**) Caracas; (

**j**) Toronto.

**Figure 30.**Boxplot of GHI forecasting error of 5 models for 5 models for: (

**a**) Al-Baha; (

**b**) Al-Jouf; (

**c**) Al-Khafji; (

**d**) Arar; (

**e**) Hail; (

**f**) Tabuk; (

**g**) Taif; (

**h**) Wadi-Addwasir; (

**i**) Caracas; (

**j**) Toronto.

**Figure 48.**Comparison of SENERGY to other models based on (

**a**) forecasting error; (

**b**) relative forecasting error.

Ref No. | Ensemble Model | Multiple Climates | Results | Main Findings |
---|---|---|---|---|

[21] | ✓ | ✓ | The ensemble model (XGBF-DNN) performed better than smart persistence, SVR, random forest (RF), XGBoost, and DNN models in hourly GHI prediction for all three locations in India and can be used for other locations. | The ensemble model (XGBF-DNN) attained RMSE = 53.79 for Jaipur, RMSE = 51.35 for New Delhi, and RMSE = 89.13 for Gangtok. |

[22] | ✓ | ✓ | Integrating of LSTM, MLP, RBF, and SVR forecasting techniques provided better performance than the individual models for Brazil and Spain in 1 h ahead PV power forecasting. | The ensemble model of LSTM, MLP, RBF, and SVR achieved MAPE = 5.36% for Spain and 4.52% for Brazil. |

[42] | ✓ | The ensemble model of ANN, LSTM, and XGBoost performed better than ANN and LSTM models alone in PV power forecast. | The ensemble model of ANN, LSTM, and XGBoost achieved RMSE = 0.74 and MAE = 0.47 with 15 min data resolution and RMSE = 0.78 and MAE = 0.59 with 1 h data resolution. | |

[23] | ✓ | ✓ | The ensemble model of GRU, LSTM, and Theta achieved better performance with Shagaya dataset than with Cocoa because of the additional weather data and it achieved better accuracy than single ML algorithms and theta model in day-ahead solar power forecast for both locations. | The ensemble model of GRU, LSTM, and Theta achieved nMAE = 0.0317 for Shagaya in Kuwait while LSTM model alone achieved nMAE = 0.0739 for Cocoa in USA, which is slightly better than the ensemble model performance with nMAE = 0.0877. |

[43] | ✓ | The ensemble model of LSTMs attained better performance than back propagation neural network (BPNN), SVM, and persistent models in day-ahead PV power forecasting. | The ensemble model of LSTMs attained RMSE = 5.68. | |

[44] | ✓ | The ensemble model of WT and bidirectional LSTM outperformed the naïve predictor, LSTM, BiLSTM, GRU and two different WT based BiLSTM models in 24 h ahead solar irradiance forecast. | The ensemble model of WT and bidirectional LSTM attained annual average RMSE = 45.61 and MAPE = 6.48%. | |

[45] | ✓ | The ensemble model of RF with cluster analysis for day-ahead solar forecasting performed better than RF alone and gradient boosted regression trees because weather classification improved the accuracy. | The ensemble model of RF with cluster analysis attained nRMSE = 8.8. | |

[46] | ✓ | The ensemble model of LSTM, NN, and SVM for solar radiation forecasting, optimized using advanced sine and cosine algorithm, outperformed all the reference models. | The ensemble model of LSTM, NN, and SVM achieved RMSE = 0.0018. | |

[24] | ✓ | The hybrid model of complete ensemble empirical mode decomposition adaptive noise (CEEMD), CNN, and LSTM to forecast hourly irradiance performed better compared to LSTM, BPNN, and SVM models as well as the hybrid CEEMDAN-LSTM, CEEMDAN-BPNN, and CEEMDAN-SVM models. | The hybrid model of CEEMD, CNN, and LSTM achieved annual RMSE = 42.84 for Tamanrasset, 43.98 for Hawaii’s Big Island, 40.60 for Denver, and 27.09 for Los Angeles. | |

[47] | ✓ | The DNN model for daily GHI prediction showed good performance with 34 cities in Turkey using all inputs (extraterrestrial radiation, sunshine duration, cloud cover, and maximum and minimum temperature). | The DNN achieved RMSE ranges from 0.52 to 1.29 for 34 cities, which represent all climatic conditions in Turkey. | |

[25] | ✓ | Statistical models’ performance of hourly solar irradiation forecasting with low to medium meteorological variabilities data is efficient while with high variability or longer forecasting horizons, bagged regression tree and RF approaches performed better. | For a medium and low variability dataset (Tilos and Ajaccio), the best 1 h ahead forecasting is MAE = 71.27 and 54.58 achieved by SVR model, whereas for a high variability dataset (Odeillo), the best result is 97.48 achieved by RF. | |

[26] | ✓ | The global DNN for hourly GHI forecasting, which was trained using data from 25 locations in the Netherlands (satellite-based measurements and weather-based forecasts) has a better average performance than other four local models. | The global DNN attained average relative RMSE = 31.31%, where the lowest relative RMSE = 29.24 for Hoek v. H. site and the highest relative RMSE = 34.55 for Deelen site. | |

[27] | ✓ | ✓ | The ensemble models (boosted trees, bagged trees, RF, and generalized RF) for short-term solar irradiance forecast outperformed SVR and Gaussian process regression. | The ensemble model achieved the best MAPE results for 4 out of 6 datasets (MAPE equals to 19.76, 42.27, 31.79, and 58.58 for CA, TX, WA, and MN respectively). |

[28] | ✓ | For hourly solar forecasting, tree-based methods were better in all-sky conditions, whereas variants of MLP and SVR were better in clear-sky and RF with quantile regression in overcast sky conditions. | Tree-based methods are superior for all-sky conditions with nRMSE ranges from 15.46% to 33.36% based on location. | |

[29] | ✓ | The LSTM model outperformed persistence, FFNN, and gradient boosting regression methods in day-ahead GHI forecasting. | The LSTM model achieved RMSE ranges from 23.6 to 37.78 for 21 locations. | |

[48] | ✓ | The global LSTM model, which was trained with international data for next-day GHI prediction, was able to predict GHI in Korea. | The global LSTM model achieved RMSE = 30 with Inchon in Korea. | |

[30] | ✓ | The ELM model, which was trained with data from 20 locations, has good performance for 15 min, 1 h, and 24 h ahead forecasting. | The ELM model achieved average RMSE = 93.82 for 20 locations for 1 h ahead forecast. |

Station No. | Station Name | Latitude (N) | Longitude (E) | Elevation (m) |
---|---|---|---|---|

1 | Al-Baha University | 20.1794 | 41.6357 | 1680 |

2 | Al-Jouf College of Technology | 29.77634 | 40.02318 | 680 |

3 | Saline Water Conversion Corporation (Al-Khafji) | 28.50676 | 48.45513 | 13 |

4 | Arar Technical Institute | 31.0274 | 40.90642 | 583 |

5 | Hail College of Technology | 27.65261 | 41.70826 | 928 |

6 | Tabuk University | 28.38287 | 36.48396 | 781 |

7 | Taif University | 21.43278 | 40.49173 | 1518 |

8 | Wadi-Addawasir College of Technology | 20.43008 | 44.89433 | 671 |

Location | Latitude (N) | Longitude (E) | Elevation (m) | Climate Class |
---|---|---|---|---|

Caracas, Venezuela | 10.49 | −66.9 | 942 | A |

Toronto, Canada | 43.65 | −79.38 | 93 | Dfb |

Time t Features | Time t−1 Features | Time t−2 Features | Time t−3 Features | Unit |
---|---|---|---|---|

GHI (output) | GHI_lag1 | GHI_lag2 | GHI_lag3 | Wh/m^{2} |

Hour_sin (HS) | DNI_lag1 | DNI_lag2 | DNI_lag3 | Wh/m^{2} |

Hour_cos (HC) | DHI_lag1 | DHI_lag2 | DHI_lag3 | Wh/m^{2} |

Day_sin (DS) | AT_lag1 | AT_lag2 | AT_lag3 | °C |

Day_cos (DC) | ZA_lag1 | ZA_lag2 | ZA_lag3 | ° |

Month_sin (MS) | WS_lag1 | WS_lag2 | WS_lag3 | m/s |

Month_cos (MC) | WD_lag1 | WD_lag2 | WD_lag3 | ° |

RH_lag1 | RH_lag2 | RH_lag3 | % | |

BP_lag1 | BP_lag2 | BP_lag3 | Pa (Saudi data)/Millibar (others) |

Tim Stamp e | GHI at t | GHI at t−1 | GHI at t−2 | GHI at t−3 |
---|---|---|---|---|

01/01/2016 7:00 | 0 | 0 | 0 | 0 |

01/01/2016 8:00 | 35.3 | 0 | 0 | 0 |

01/01/2016 9:00 | 236.2 | 35.3 | 0 | 0 |

01/01/2016 10:00 | 468.8 | 236.2 | 35.3 | 0 |

01/01/2016 11:00 | 609.6 | 468.8 | 236.2 | 35.3 |

01/01/2016 12:00 | 688.7 | 609.6 | 468.8 | 236.2 |

01/01/2016 13:00 | 686.8 | 688.7 | 609.6 | 468.8 |

01/01/2016 14:00 | 635.6 | 686.8 | 688.7 | 609.6 |

01/01/2016 15:00 | 522.7 | 635.6 | 686.8 | 688.7 |

01/01/2016 16:00 | 361.3 | 522.7 | 635.6 | 686.8 |

01/01/2016 17:00 | 166.2 | 361.3 | 522.7 | 635.6 |

01/01/2016 18:00 | 15.6 | 166.2 | 361.3 | 522.7 |

Location | Total Hourly Records | Missing Days | GHI Mean | GHI SD | GHI Var |
---|---|---|---|---|---|

Al-Baha | Train: 6227 | 635 days | 574.67 | 323.90 | 104,896.29 |

Val: 3056 | 552.10 | 325.90 | 106,176.30 | ||

Test: 2247 | 582.09 | 311.16 | 96,780.11 | ||

Al-Jouf | Train: 8600 | 363 days | 554.11 | 307.66 | 94,643.25 |

Val: 2991 | 547.92 | 306.49 | 93,901.92 | ||

Test: 2554 | 528.14 | 296.47 | 87,858.12 | ||

Al-Khafji | Train: 4618 | 970 days (Year 2019) | 504.81 | 288.56 | 83,245.88 |

Val: 2363 | 555.17 | 308.66 | 95,231.29 | ||

Test: 2110 | 486.59 | 275.73 | 75,991.13 | ||

Arar | Train: 8339 | 575 days | 546.71 | 310.06 | 96,128.23 |

Val: 3589 | 537.73 | 300.20 | 90,097.23 | ||

Test: 1357 | 485.46 | 295.04 | 86,983.40 | ||

Hail | Train: 8723 | 271 days | 552.26 | 311.69 | 97,140.65 |

Val: 3260 | 544.05 | 310.67 | 96,486.20 | ||

Test: 2561 | 543.77 | 303.82 | 92,270.30 | ||

Tabuk | Train: 7576 | 542 days | 593.27 | 310.35 | 96,307.42 |

Val: 3100 | 579.62 | 303.93 | 92,342.88 | ||

Test: 1937 | 498.03 | 261.73 | 68,465.05 | ||

Taif | Train: 8618 | 272 days | 580.83 | 321.62 | 103,424.30 |

Val: 3386 | 562.14 | 308.42 | 95,094.37 | ||

Test: 2543 | 567.62 | 308.47 | 95,115.01 | ||

Wadi-Addawasir | Train: 9199 | 242 days | 584.98 | 309.00 | 95,474.22 |

Val: 3450 | 579.24 | 306.12 | 93,684.80 | ||

Test: 2551 | 578.02 | 301.69 | 90,982.42 | ||

Caracas | Train: 10,112 | 0 days | 499.28 | 284.48 | 80,922.07 |

Val: 3428 | 505.95 | 288.71 | 83,327.90 | ||

Test: 3428 | 524.82 | 297.12 | 88,255.24 | ||

Toronto | Train: 9892 | 0 days | 381.15 | 273.39 | 74,732.91 |

Val: 3392 | 336.74 | 266.95 | 71,242.70 | ||

Test: 3388 | 366.77 | 278.11 | 77,322.36 | ||

All | Train: 81,904 | 3870 days | - | - | - |

Val: 32,015 | |||||

Test: 24,676 |

Al-Jouf | Al-Khafji | Wadi-Addawasir | Caracas | Toronto | |||||
---|---|---|---|---|---|---|---|---|---|

Feature | PC | Feature | PC | Feature | PC | Feature | PC | Feature | PC |

GHI_lag1 | 0.88 | GHI_lag1 | 0.87 | HC | −0.91 | HC | −0.80 | GHI_lag1 | 0.87 |

HC | −0.82 | HC | −0.81 | GHI_lag1 | 0.86 | GHI_lag1 | 0.76 | ZA_lag1 | −0.68 |

ZA_lag1 | −0.82 | ZA_lag1 | −0.78 | ZA_lag1 | −0.80 | ZA_lag1 | −0.61 | DNI_lag1 | 0.64 |

DNI_lag1 | 0.59 | DNI_lag1 | 0.63 | HS | 0.53 | HS | 0.58 | GHI_lag2 | 0.64 |

HS | 0.47 | HS | 0.51 | DNI_lag1 | 0.53 | DNI_lag1 | 0.49 | DNI_lag2 | 0.54 |

GHI_lag2 | 0.47 | GHI_lag2 | 0.47 | HC | −0.51 |

Al-Jouf | Al-Khafji | Wadi-Addawasir | Caracas | Toronto | |||||
---|---|---|---|---|---|---|---|---|---|

FFS | BFE | FFS | BFE | FFS | BFE | FFS | BFE | FFS | BFE |

HS | HS | HS | HS | HS | HS | HS | HS | MS | HS |

HC | HC | WS_lag1 | DHI_lag1 | HC | HC | HC | HC | HS | DHI_lag1 |

DHI_lag1 | DHI_lag1 | DHI_lag1 | DNI_lag1 | DHI_lag1 | DHI_lag1 | DHI_lag1 | DHI_lag1 | GHI_lag1 | DNI_lag1 |

DNI_lag1 | DNI_lag1 | DNI_lag1 | GHI_lag1 | DNI_lag1 | DNI_lag1 | DNI_lag1 | DNI_lag1 | ZA_lag1 | GHI_lag1 |

GHI_lag1 | GHI_lag1 | GHI_lag1 | BP_lag1 | GHI_lag1 | GHI_lag1 | GHI_lag1 | GHI_lag1 | WS_lag1 | AT_lag1 |

ZA_lag1 | ZA_lag1 | ZA_lag1 | DHI_lag2 | DHI_lag2 | DHI_lag2 | RH_lag1 | RH_lag1 | WS_lag3 | ZA_lag2 |

DHI_lag2 | DHI_lag2 | DHI_lag2 | DNI_lag2 | ZA_lag3 | ZA_lag3 | GHI_lag2 | DNI_lag2 | DNI_lag2 | DHI_lag3 |

DNI_lag2 | DNI_lag2 | DHI_lag3 | GHI_lag2 | GHI_lag3 | GHI_lag2 | ZA_lag2 | ZA_lag2 | GHI_lag3 | DNI_lag3 |

GHI_lag3 | GHI_lag2 | DNI_lag3 | ZA_lag2 | ZA_lag1 | ZA_lag2 | WS_lag3 | WS_lag3 | ZA_lag3 | GHI_lag3 |

ZA_lag3 | AT_lag1 | GHI_lag3 | GHI_lag3 | RH_lag1 | DNI_lag2 | AT_lag3 | AT_lag3 | RH_lag3 | AT_lag3 |

Model | Batch Size | Layers | Learning Rate | Number of Epochs | Optimization |
---|---|---|---|---|---|

LSTM | 256 | 3 hidden layers with 128 hidden states, 1 dense layer | 0.001 | 100 | Dropout = 0.2, ReLU function, Weight decay = 0.000001, Adam |

GRU | 256 | 3 hidden layers with 128 hidden states,1 dense layer | 0.001 | 100 | Dropout = 0.2, ReLU function, Weight decay = 0.000001, Adam |

CNN | 64 | 2 conv layers with 10 and 5 filters, 1 max-pooling layer, 2 dense layers | 0.001 | 100 | Dropout = 0.2, ReLU function, Adam, batch normalization |

CNN-BiLSTM | 64 | 2 conv layers with 10 and 5 filters, 1 max-pooling layer, 1 BiLSTM layer, 2 dense layers | 0.001 | 100 | Dropout = 0.2, ReLU function, Adam, batch normalization |

LSTM-AE | 256 | 4 LSTM layers with 128 hidden states, 1 dense layer | 0.001 | 100 | ReLU function, weight decay = 0.000001, Adam |

Model | Precision | Recall | F1-score | Support |
---|---|---|---|---|

CNN-BiLSTM | 66% | 36% | 47% | 809 |

LSTM-AE | 83% | 94% | 88% | 2691 |

Accuracy | 81% | 3500 | ||

Macro average | 75% | 65% | 68% | 3500 |

Weighted average | 79% | 81% | 79% | 3500 |

FE CNN-BiLSTM | FE LSTM-AE | FE Best Model | G/L CNN-BiLSTM | G/L LSTM-AE |
---|---|---|---|---|

67.73 | 4.83 | 4.83 | 62.90 | 0 |

128.60 | 44.41 | 128.60 | 0 | −84.19 |

0.47 | 29.12 | 29.12 | −28.65 | 0 |

Ref No. | Location | GHI Mean | GHI SD | Climate | Weather Data | Model |
---|---|---|---|---|---|---|

[21] | Jaipur | NA | NA | - Cwa
- Cwb
- Bsh
| ✓ | Ensemble model of XGBF-DNN |

New Delhi | ||||||

Gangtok | ||||||

[24] | Los Angeles | 217.37 | 291.73 | - Csb,
- BSk
- Af
- BWk
| ✗ | Hybrid model of CEEMDAN-CNN-LSTM |

Denver | 203.33 | 276.40 | ||||

Hawaii’s Big Island | 220.12 | 307.79 | ||||

Tamanrasset | 269.98 | 361.83 | ||||

[25] | Ajaccio | NA | NA | - Csa
- Csb
| ✗ | ARMA RF |

Tilos | ||||||

Odeillo | ||||||

[27] | CA | NA | NA | - BSk
- Cfa
- Cfb
- Am
- Dfb
- Dfa
| ✓ | Generalized random forest |

TX | ||||||

WA | ||||||

FL | ||||||

PA | ||||||

MN | ||||||

[28] | Bondville | 398.04 | 284.66 | - Cfa
- BWk
- BSk
- Cfb
- Dfa
| ✗ | 68 machine learning algorithms (Cubist model is the best in most cases) |

Desert Rock | 517.72 | 314.73 | ||||

Fort Peck | 368.17 | 277.33 | ||||

Goodwin Creek | 442.77 | 289 | ||||

Penn. State Uni | 384.31 | 277.24 | ||||

Sioux Falls | 406.94 | 277.55 | ||||

Table Mountain | 412.19 | 287.97 | ||||

[30] | Tucson | 532.5 | NA | - Bsh
- Cfa
- A
- Dfc
| ✗ | ELM |

Bermuda | 417.1 | |||||

Brasilia | 475.6 | |||||

Sonnblick | 347.2 | |||||

Solar Village | 580.9 | |||||

Golden | 459.4 | |||||

Darwin | 516.4 | |||||

Ny-Alesund | 184.3 | |||||

Toravere | 256.9 | |||||

Lerwick | 198.3 | |||||

This work | Al-Baha | 582.09 | 311.16 | - BWh
- A
- Dfb
| ✓ | LSTM GRU CNN CNN-BiLSTM LSTM-AE |

Al-Jouf | 528.14 | 296.47 | ||||

Al-Khafji | 486.59 | 275.73 | ||||

Arar | 485.46 | 295.04 | ||||

Hail | 543.77 | 303.82 | ||||

Tabuk | 498.03 | 261.73 | ||||

Taif | 567.62 | 308.47 | ||||

Wadi-Addawasir | 578.02 | 301.69 | ||||

Caracas | 366.77 | 271.11 | ||||

Toronto | 524.82 | 297.12 | ||||

Ref [30] has 20 locations, we present data from 10 locations from various climates for simplicity. NA: Not available. |

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## Share and Cite

**MDPI and ACS Style**

Alkhayat, G.; Hasan, S.H.; Mehmood, R. SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting. *Energies* **2022**, *15*, 6659.
https://doi.org/10.3390/en15186659

**AMA Style**

Alkhayat G, Hasan SH, Mehmood R. SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting. *Energies*. 2022; 15(18):6659.
https://doi.org/10.3390/en15186659

**Chicago/Turabian Style**

Alkhayat, Ghadah, Syed Hamid Hasan, and Rashid Mehmood. 2022. "SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting" *Energies* 15, no. 18: 6659.
https://doi.org/10.3390/en15186659