# Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction

^{*}

## Abstract

**:**

## 1. Introduction

- A multi-layer perceptron model with dynamic feature selection is developed for hour-ahead forecasting of a nanogrid;
- Peak loads of a nanogrid are identified fast and accurately based on the predicted load for potential energy cost saving through load shifting;
- Numerical testing results demonstrate the performance of the developed model with MSE of 0.03 kW, MAPE of 9%, and CV of 11.9%, and the achievement of 20% energy cost savings through shifting peak loads throughout the day.

## 2. Literature Review

#### 2.1. Load Forecasting

#### 2.2. Peak Load

## 3. Methodology

#### 3.1. Pre-Processing

#### 3.2. Feature Selection

#### 3.3. Network Modelling

_{n}), input (x

_{n}), and activation function (f) and bias (b) as shown in Figure 3. Inputs coupled with weights are passed into an activation function to determine the output (y).

#### 3.4. Pseudocode

Algorithm 1: for the ANN Model | |

Input: Input features i.e., Meteorological data and Electric load | |

Output: Hourly forecasted electric load and daily energy cost savings | |

1. | Get weather and electric load data |

2. | Pre-process the data by converting electric data into hourly electric data |

3. | Extract slope, t-1 load, and “rising or falling edge” parameter from the electric load |

4. | Get Pearson correlation Coefficients (µ_{f}) for weather data and its corresponding electric load |

5. | Apply K-means with K = 2 on the set of coefficients [µ_{1}, µ_{2},...,µ_{f}] and determine the mid-point of two centroids (m) |

6. | for (t < Number of Potential features) |

7. | if (µ_{f} > m) |

8. | Feature (f) is selected as an input feature |

9. | else |

10. | Feature (f) is dropped |

11. | t = t + 1 |

12. | end for |

13. | Divide data (Input feature and electric load) into training and testing sets |

14. | Initialize ANN model |

15. | Training time starts |

16. | while (1) |

17. | Run the model |

18. | Losses are calculated |

19. | Tune the hyperparameters if needed |

20. | if1(Losses(t)-Losses(t-1)< ε) |

21. | count = count + 1 |

22. | if2 (count > β) |

23. | Break |

24. | end if2 |

25. | end if1 |

26. | end while |

27. | Training time stops and total training time is calculated (time) |

28. | Model tested on test data for hourly load forecasting (L_{hour}) |

29. | MAPE, MSE, CV, and RMSE are calculated |

30. | if (L_{hour} > 1.5*average (L_{previous_hours}) & time is within range of peak hours) |

31. | L_{hour} is considered peak load |

32. | end if |

33. | Savings are calculated based on the potential shifting of L_{hour} to an off-peak hour. |

34. | Repeat from point 4 for load forecasting of the next hours |

#### 3.5. Model Evaluation Criterion

## 4. Results

#### 4.1. Dataset Description/Set-Up

#### 4.2. Hyperparameters and Features

#### 4.3. Load Forecasting

#### 4.4. Cost Savings

## 5. Conclusions

- Apply optimization methods to decide load shifting strategies and, thus, to determine penitential energy cost savings for a nanogrid;
- Optimize the pricing mechanism for energy transactions within the nanogrid network by using the developed load forecasting model.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ANN | Artificial Neural Network |

ARIMA | Autoregressive Integrated Moving Average |

Bi-LSTM | Bi-directional Long Short-Term Memory |

CNN | Convolutional Neural Network |

CV | Co-efficient of variance |

GRU | Gated Recurrent Unit |

GBRT | Gradient Boosted Regression Trees |

LSTM | Long Short-Term Memory |

MAE | Mean Absolute Error |

MAPE | Mean Absolute Percentage Error |

MG | microgrid |

MSE | Mean Squared Error |

NG | nanogrid |

PAR | Peak to Average Power Ratio |

PSO | Particle Swarm Optimization |

P2P | Peer-to-Peer |

PV | Photovoltaic |

RMSE | Root Mean Squared Error |

ReLU | Rectified Linear Unit |

RNN | Recurrent Neural Networks |

STLF | Short-Term Load Forecasting |

SVM | Support Vector Machine |

SVR | Support Vector Regression |

SARIMA | Seasonal Autoregressive Integrated Moving Average |

WNN | Wavelet Neural Network |

## Appendix A

#### Appendix A.1. LSTM

**Figure A1.**LSTM structure [56].

#### Appendix A.2. GRU

**Figure A3.**The basic structure of GRU [57].

#### Appendix A.3. Bi-LSTM

## Appendix B

## Appendix C

Algorithm A1: Pseudo-code for LSTM/GRU/Bi-LSTM Models | |

Input: Electric load time series | |

Output: Hourly forecasted electric load and daily energy cost savings | |

1. | Get Electric load data |

2. | Pre-process the data by converting electric data into hourly electric data |

3. | Divide data (Input feature and electric load) into training and testing sets |

4. | Initialize LSTM/GRU/Bi-LSTM model |

5. | Training time starts |

6. | while (1) |

7. | Run the model |

8. | Losses are calculated |

9. | Tune the hyperparameters if needed |

10. | if1(Losses(t)-Losses(t-1)< ε) |

11. | count = count + 1 |

12. | if2 (count > β) |

13. | Break |

14. | end if2 |

15. | end if1 |

16. | end while |

17. | Training time stops and total training time is calculated (time) |

18. | Model tested on test data for hourly load forecasting (L_{hour}) |

19. | MAPE, MSE, CV, and RMSE are calculated |

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**Figure 3.**Basic structure of ANN neuron [38].

**Figure 4.**ANN network [52].

Paper Title | Classification | Algorithm | Summary/Results | Area |
---|---|---|---|---|

[6] A novel hybrid forecasting scheme for electricity demand time series’ | Statistical | ARIMA | Spain’s grid load forecasting has been performed while incorporating non-linear effects of temperature and special days for hourly 1 to 10 days ahead demand. | Grid |

[28] Regression-Based Methods for Daily Peak Load Forecasting in South Korea | Statistical | ARIMA | Implementation of ARIMA in a South Korean grid showed a MAPE of 1.95%. | Grid |

[7] Microgrid Load Forecasting Based on Improved Long Short-Term Memory Network | ML | LSTM | The model has been implemented for a micro-grid utilizing 5 years of load data. Results show improvement by reduction of MAPE from 8% to within 4%. | Microgrid |

[10] Enhanced Short-Term Load Forecasting Using Artificial Neural Networks | ML | ANN | This work predicts load forecasting for Greek Intercontinental Power System with various scaling methods for input data. Based on the scaling method, MAPE changes from 2.73% to 1.76%. | Grid |

[11] Deep Learning for Short-Term Load Forecasting: Industrial Consumer Case Study | ML & Statistical | GRU, ARIMA, LSTM, RNN and their combinations | Woodworking factory’s load was predicted using numerous mentioned methods. Amongst these, GRU outperformed others with a MAPE of 4.82%. Exogenous and lagged load data were used as input features. | Factory (Microgrid) |

[29] Short and mid-term load forecasting using machine learning models | ML & Statistical | LR, SVR, GBRT | New York Independent System Operators (NYISO) dataset was used for the implementation of these algorithms and a comparison, on basis of MAPE, was drawn. Previous load and meteorological data were used as input features. It was found that the hybrid model AdaBoost and GBRT showed improved results with the MAPE of 2.27%. | Grid |

[9] Load Forecasting for Different Prediction Horizons using ANN and ARIMA models | ML & Statistical | ANN & ARIMA | Microgrid load has been trained using ANN and compared with ARIMA. ANN captures more random behavior which is corroborated by the results: ANN is 0.5% more accurate than ARIMA in day-ahead prediction and 3.5% more accurate in an hour-ahead prediction. | Microgrid |

Parameters | ANN | LSTM | GRU | BI-LSTM |
---|---|---|---|---|

Input Features | Meteorological and electrical load features | Electric load time series | Electric load time series | Electric load time series |

Output | Hourly load forecast | Hourly load forecast | Hourly load forecast | Hourly load forecast |

Number of hidden layers | 3 | 1 | 1 | 1 |

Number of neurons in 1st hidden layer | 30 | - | - | - |

Number of neurons in 2nd hidden layer | 30 | - | - | - |

Number of neurons in 3rd hidden layer | 20 | - | - | - |

Number of internal nodes | - | 200 | 200 | 200 |

Learning Rate | 0.0001 | 0.001 | 0.001 | 0.001 |

Internal Optimizer | Adam | Adam | Adam | Adam |

Epoch | 300 | 10 | 10 | 10 |

Activation Function | ReLu | ReLu | ReLu | ReLu |

Batch Size | 32 | 32 | 32 | 32 |

Regularization | l-1 on 3rd hidden layer | - | - | - |

Weight Initialization | Random | Random | Random | Random |

Evaluation Parameters | ANN | LSTM | GRU | Bi-Directional LSTM |
---|---|---|---|---|

MSE (KW) | 0.03 | 0.37 | 0.34 | 0.35 |

RMSE (KW) | 0.17 | 0.60 | 0.55 | 0.59 |

MAPE (%) | 9.00 | 28.0 | 26.1 | 25.0 |

CV (%) | 11.9 | 33.7 | 32.3 | 32.8 |

Training Time (min) | 7.50 | 39.40 | 27.6 | 38.7 |

Date | Hour Indices for Peak Load | Daily Energy Cost without Peak Shifting ($) | Daily Energy Cost with Peak Shifting ($) | Energy Cost Saving (%) |
---|---|---|---|---|

2016 August 20 | 4:00 PM | 0.28 | 0.22 | 21.0% |

2016 August 21 | 12:00 PM | 0.77 | 0.58 | 24.3% |

2016 August 22 | 6:00 PM | 0.12 | 0.09 | 24.3% |

2016 August 23 | 2:00 PM, 3:00 PM, 4:00 PM, 5:00 PM, 9:00 PM | 0.51 | 0.41 | 19.2% |

2016 August 24 | 3:00 PM, 4:00 PM, 5:00 PM, 6:00 PM, 7:00 PM | 1.08 | 0.81 | 24.3% |

2016 August 25 | 2:00 PM, 3:00 PM, 5:00 PM | 0.75 | 0.56 | 25.0% |

2016 August 26 | 3:00 PM, 4:00 PM, 6:00 PM | 0.33 | 0.26 | 20.9% |

Week | Weekly Energy Cost without Peak Shifting ($) | Weekly Energy Cost with Peak Shifting ($) | Cost Saving (%) | |
---|---|---|---|---|

Extreme Summer | 2016 July 15 to 2016 July 21 | 6.03 | 5.02 | 16.7 |

Extreme Winter | 2016 February 1 to 2016 August 1 | 28.4 | 25.9 | 8.85 |

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## Share and Cite

**MDPI and ACS Style**

Kumar, A.; Yan, B.; Bilton, A.
Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction. *Energies* **2022**, *15*, 6721.
https://doi.org/10.3390/en15186721

**AMA Style**

Kumar A, Yan B, Bilton A.
Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction. *Energies*. 2022; 15(18):6721.
https://doi.org/10.3390/en15186721

**Chicago/Turabian Style**

Kumar, Akash, Bing Yan, and Ace Bilton.
2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction" *Energies* 15, no. 18: 6721.
https://doi.org/10.3390/en15186721