| Title | Machine learning-based model for prediction of power consumption in smart grid- smart way towards smart city |
|---|---|
| ID_Doc | 36804 |
| Authors | Tiwari, S; Jain, A; Ahmed, NMOS; Charu; Alkwai, LM; Dafhalla, AKY; Hamad, SAS |
| Title | Machine learning-based model for prediction of power consumption in smart grid- smart way towards smart city |
| Year | 2022 |
| Published | Expert Systems, 39, 5 |
| Abstract | A smart city is an idea that is realized by the computing of a large amount of data collected through sensors, cameras, and other electronic methods to provide services, manage resources and solve daily life problems. The transformation of the conventional grid to a smart grid is one step in the direction towards smart city realization. An electric grid is composed of control stations, generation centres, transformers, communication lines, and distributors, which helps in transferring power from the power station to domestic and commercial consumers. Present electric grids are not smart enough that they can estimate the varying power requirement of the consumer. Also, these conventional grids are not enough robust and scalable. This has become the motivation for shifting from a conventional grid to a smart grid. The smart grid is a kind of power grid, which is robust and adapts itself to the varying needs of the consumer and self-healing in nature. In this way, the transformation from a conventional grid to a smart grid will help the government to make a smart city. The emergence of machine learning has helped in the prediction of the stability of the grid under the dynamically changing requirement of the consumer. Also, the usage of a variety of sensors will help in the collection of real-time consumption data. Through machine learning algorithms, we can gain an insight view of the collected data. This has helped the smart grid to convert into a robust smart grid, as this will help in avoiding the situation of failure. In this work, the authors have applied logistic regression, decision tree, support vector machine, linear discriminant analysis, quadratic discriminant analysis, naive Bayes, random forest, and k-nearest neighbour algorithms to predict the stability of the grid. The authors have used the smart grid stability dataset freely available on Kaggle to train and test the models. It has been found that a model designed using the support vector machine algorithm has given the most accurate result. |
Similar Articles
| ID | Score | Article |
|---|---|---|
77594
|
Otte, M; Rohjans, S; Andrén, FP; Strasser, TI Applying Machine Learning Concepts to Enhance the Smart Grid Engineering Process(2019) | |
| 39599
|
Shah, SSM; Meganathan, S Machine learning approach for power consumption model based on monsoon data for smart cities applications(2021)Computational Intelligence, 37, 3 | |
| 41548
|
Wang, SR; Song, AF; Qian, YF Predicting Smart Cities? Electricity Demands Using K-Means Clustering Algorithm in Smart Grid(2023)Computer Science And Information Systems, 20, 2 | |
| 37945
|
Tang, ZQ; Xie, HP; Du, CQ; Liu, YY; Khalaf, OI; Allimuthu, UK Machine Learning Assisted Energy Optimization in Smart Grid for Smart City Applications(2022)Journal Of Interconnection Networks, 22, Supp03 | |
| 40056
|
Chen, YL; Fan, JQ; Deng, Z; Du, B; Huang, XH; Gui, QR PR-KELM: Icing level prediction for transmission lines in smart grid(2020) | |
| 43952
|
Horák, M Prediction of Household Consumption Based on the Model(2019) | |
| 45456
|
Li, Y An efficient mathematical model to process data of smart city(2014) | |
| 43540
|
Hu, YC; Lin, YH; Gururaj, HL Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home(2021)Processes, 9, 9 | |
| 64805
|
Jahan, IS; Blazek, V; Misak, S; Snasel, V; Prokop, L Forecasting of Power Quality Parameters Based on Meteorological Data in Small-Scale Household Off-Grid Systems(2022)Energies, 15, 14 | |
| 42548
|
Mahmood, D; Latif, S; Anwar, A; Hussain, SJ; Jhanjhi, NZ; Sama, NU; Humayun, M Utilization of ICT and AI techniques in harnessing residential energy consumption for an energy-aware smart city: A review(2021)International Journal Of Advanced And Applied Sciences, 8, 7 |