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

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 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

- Wang, P.; Liu, B.; Hong, T. Electric load forecasting with recency effect: A big data approach. Int. J. Forecast.
**2016**, 32, 585–597. [Google Scholar] [CrossRef] [Green Version] - Ludwig, N.; Feuerriegel, S.; Neumann, D. Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests. J. Decis. Syst.
**2015**, 24, 19–36. [Google Scholar] [CrossRef] - Lago, J.; de Ridder, F.; de Schutter, B. Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy
**2018**, 221, 386–405. [Google Scholar] [CrossRef] - Wang, K.; Xu, C.; Zhang, Y.; Guo, S.; Zomaya, A. Robust big data analytics for electricity price forecasting in the SG. IEEE Trans. Big Data
**2017**. [Google Scholar] [CrossRef] - Haupt, S.E.; Kosović, B. Variable generation power forecasting as a big data problem. IEEE Trans. Sustain. Energy
**2017**, 8, 725–732. [Google Scholar] [CrossRef] - Chen, J.; Yang, Y.; Wang, Z.; Li, Y.; Guo, Y.; Cui, J.; Zhu, D. Distribution fault outage cost evaluation and hotspot area recognition based on big data. J. Eng.
**2017**, 2017, 768–772. [Google Scholar] [CrossRef] - Kenner, S.; Thaler, R.; Kucera, M.; Volbert, K.; Waas, T. Comparison of SG architectures for monitoring and analyzing power grid data via Modbus and REST. EURASIP J. Embed. Syst.
**2017**, 2017, 12. [Google Scholar] [CrossRef] [Green Version] - Tang, Y.; Yang, J. Dynamic event monitoring using unsupervised feature learning towards SG big data. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14–19 May 2017; pp. 1480–1487. [Google Scholar]
- Grolinger, K.; L’Heureux, A.; Capretz, M.A.M.; Seewald, L. Energy forecasting for event venues: Big data and prediction accuracy. Energy Build.
**2016**, 112, 222–233. [Google Scholar] [CrossRef] [Green Version] - Zhou, K.; Fu, C.; Yang, S. Big data driven smart energy management: From big data to big insights. Renew. Sustain. Energy Rev.
**2016**, 56, 215–225. [Google Scholar] [CrossRef] - Koseleva, N.; Ropaite, G. Big data in building energy efficiency: Understanding of big data and main challenges. Procedia Eng.
**2017**, 172, 544–549. [Google Scholar] [CrossRef] - Singh, S.; Yassine, A. Mining energy consumption behavior patterns for households in SG. IEEE Trans. Emerg. Top. Comput.
**2017**. [Google Scholar] [CrossRef] - Daut, M.A.M.; Hassan, M.Y.; Abdullah, H.; Rahman, H.A.; Abdullah, M.P.; Hussin, F. Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renew. Sustain. Energy Rev.
**2017**, 70, 1108–1118. [Google Scholar] [CrossRef] - Kezunovic, M.; Xie, L.; Grijalva, S. The role of big data in improving power system operation and protection. In Proceedings of the 2013 IREP Symposium on Bulk Power System Dynamics and Control-IX Optimization, Security and Control of the Emerging Power Grid (IREP), Rethymnon, Crete, Greece, 25–30 August 2013; pp. 1–9. [Google Scholar]
- Jaradat, M.; Jarrah, M.; Bousselham, A.; Jararweh, Y.; Al-Ayyoub, M. The internet of energy: Smart sensor networks and big data management for SG. Procedia Comput. Sci.
**2015**, 56, 592–597. [Google Scholar] [CrossRef] [Green Version] - Mi, J.; Wang, K.; Liu, B.; Ding, F.; Sun, Y.; Huang, H. A multiobjective evolution algorithm based rule certainty updating strategy in big data environment. In Proceedings of the GLOBECOM 2017—2017 IEEE Global Communications Conference, Singapore, 4–8 December 2017; pp. 1–6. [Google Scholar]
- Xu, C.; Wang, K.; Li, P.; Xia, R.; Guo, S.; Guo, M. Renewable Energy-Aware Big Data Analytics in Geo-distributed Data Centers with Reinforcement Learning. IEEE Trans. Netw. Sci. Eng.
**2018**. [Google Scholar] [CrossRef] - Moghaddass, R.; Wang, J. A hierarchical framework for SG anomaly detection using large-scale smart meter data. IEEE Trans. Smart Grid
**2017**, 9, 5820–5830. [Google Scholar] [CrossRef] - Munshi, A.A.; Yasser, A.-I.M. Big data framework for analytics in SGs. Electr. Power Syst. Res.
**2017**, 151, 369–380. [Google Scholar] [CrossRef] - Munshi, A.A.; Mohamed, Y.A.I. Cloud-based visual analytics for SGs big data. In Proceedings of the 2016 IEEE Power and Energy Society on Innovative Smart Grid Technologies Conference (ISGT), Minneapolis, MN, USA, 6–9 September 2016. [Google Scholar]
- He, X.; Ai, Q.; Qiu, R.C.; Huang, W.; Piao, L.; Liu, H. A big data architecture design for SGs based on random matrix theory. IEEE Trans. Smart Grid
**2017**, 8, 674–686. [Google Scholar] - Hou, W.; Ning, Z.; Guo, L.; Zhang, X. Temporal, Functional and Spatial Big Data Computing Framework for Large-Scale Smart Grid. IEEE Trans. Emerg. Top. Comput.
**2017**, 7, 369–379. [Google Scholar] [CrossRef] - Alamaniotis, M.; Gatsis, N.; Tsoukalas, L.H. Virtual Budget: Integration of electricity load and price anticipation for load morphing in price-directed energy utilization. Electr. Power Syst. Res.
**2018**, 158, 284–296. [Google Scholar] [CrossRef] - Amjady, N.; Daraeepour, A. Mixed price and load forecasting of electricity markets by a new iterative prediction method. Electr. Power Syst. Res.
**2009**, 79, 1329–1336. [Google Scholar] [CrossRef] - Alamaniotis, M.; Bargiotas, D.; Bourbakis, N.G.; Tsoukalas, L.H. Genetic optimal regression of relevance vector machines for electricity pricing signal forecasting in smart grids. IEEE Trans. Smart Grid
**2015**, 6, 2997–3005. [Google Scholar] [CrossRef] - Zhang, Y.; Quan, Z.; Caixin, S.; Shaolan, L.; Yuming, L.; Yang, S. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans. Power Syst.
**2008**, 23, 853–858. [Google Scholar] [CrossRef] - Khotanzad, A.; Zhou, E.; Elragal, H. A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment. IEEE Trans. Power Syst.
**2002**, 17, 1273–1282. [Google Scholar] [CrossRef] - Rabiya, K.; Javaid, N. A short-term load and price forecasting using optimized LSTM in SG. In Proceedings of the IEEE ICC’20—NGNI Symposium, Shanghai, China, 20–24 May 2019. [Google Scholar]
- Zheng, J.; Xu, C.; Zhang, Z.; Li, X. Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In Proceedings of the 2017 51st Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 22–24 March 2017; pp. 1–6. [Google Scholar]
- Elia. Grid Data. Available online: http://www.elia.be/en/grid-data/dashboard (accessed on 2 April 2018).
- Cousineau, D.; Chartier, S. Outliers detection and treatment: A review. Int. J. Psychol. Res.
**2010**, 3, 58–67. [Google Scholar] [CrossRef] - Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput.
**1997**, 9, 1735–1780. [Google Scholar] [CrossRef] - Gers, F.A.; Schmidhuber, J.; Cummins, F. Learning to Forget: Continual Prediction with LSTM. In Proceedings of the 9th International Conference on Artificial Neural Networks: ICANN ’99, Edinburgh, UK, 7–10 September 1999; pp. 850–855. [Google Scholar]
- Rao, R. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput.
**2016**, 7, 19–34. [Google Scholar]

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 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**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