# Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids

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## Abstract

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## 1. Introduction

#### 1.1. Related Work

#### 1.2. Motivation

#### 1.3. Contributions

- For electricity demand and price prediction, a multi-variable forecasting model Jaya-LSTM is proposed.
- The data set is preprocessed and cleaned before passing it on to the forecasting model. Outliers and missing values are removed and the values of input variables are normalized to make them comparable.
- To increase the forecasting accuracy of the model and get the best forecasted values, hyperparameters are tuned using the Jaya optimization algorithm. This is used as it is simple to apply and does not require deep knowledge and great optimization performance.
- The proposed model is tested on two separate data sets of price and demand of electricity. For evaluation, we have compared this model with SVR and classic LSTM. The performance is measured on the basis of two performance metrics, i.e., MAE and MAPE.

#### 1.4. Organization of Paper

## 2. Problem Statement

## 3. Proposed Solution

#### 3.1. Input Data

#### 3.2. Data Preprocessing

- Data cleaning is the first step. The missing values are filled or removed from the data set along with smoothing noise and inconsistency of data.
- In data integration, conflicts between data are resolved while integrating the data of different representations.
- Data transformation is also an important step, where the values of variables are normalized between a common interval to make them comparable.
- In the data reduction step, data is reduced by excluding irrelevant and duplicate information.

#### 3.2.1. Remove Missing Values

#### 3.2.2. Remove Outliers

#### 3.2.3. Normalize Data

#### 3.3. Forecasting Algorithm

## 4. Simulation Results

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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JLSTM | LSTM | SVM | |
---|---|---|---|

RMSE (demand) | 0.02 | 0.06 | 0.10 |

MAE (demand) | 0.1 | 1.4 | 0.95 |

RMSE (price) | 0.04 | 0.08 | 0.15 |

MAE (price) | 0.47 | 1.56 | 1.09 |

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**MDPI and ACS Style**

Khalid, R.; Javaid, N.; Al-zahrani, F.A.; Aurangzeb, K.; Qazi, E.-u.-H.; Ashfaq, T. Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids. *Entropy* **2020**, *22*, 10.
https://doi.org/10.3390/e22010010

**AMA Style**

Khalid R, Javaid N, Al-zahrani FA, Aurangzeb K, Qazi E-u-H, Ashfaq T. Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids. *Entropy*. 2020; 22(1):10.
https://doi.org/10.3390/e22010010

**Chicago/Turabian Style**

Khalid, Rabiya, Nadeem Javaid, Fahad A. Al-zahrani, Khursheed Aurangzeb, Emad-ul-Haq Qazi, and Tehreem Ashfaq. 2020. "Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids" *Entropy* 22, no. 1: 10.
https://doi.org/10.3390/e22010010