Electricity load forecasting and feature extraction in smart grid using neural networks. (December 2021)

• Record Type: Journal Article
• Authors: Jha, Nishant; Prashar, Deepak; Rashid, Mamoon; Gupta, Sachin Kumar; Saket, R.K.
• Is Part Of: Computers & electrical engineering. Volume 96:Part A(2021)
• Publication Date: 2021-12
• Subjects: Smart grids -- Load forecasting -- Statistical technique -- Artificial neural network (ANN) -- Long short term memory (LSTM) -- Random forest -- Mean square error (MSE); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107479
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Systematic framework is established that formalizes the scope of the smart grids. A deep learning-based approach is proposed for predicting load in smart grids quite accurately with use of features of various neural networks for efficient load balancing in smart grids. Use of the proposed approach to efficiently determine the load forecasting characteristics in macro grids and microgrids. Comparative analysis of proposed work with state-of-the-art for load forecasting and feature extraction in smart grids. Abstract: Load forecasting plays an essential role in effective energy planning and distribution in a smart grid. However, due to the unpredictable and non-linear structure of smart grids and large datasets' complex nature, accurate load forecasting is still challenging. Statistical techniques are being used for a long time for load forecasting, but it is inefficient. This paper tries to resolve challenges imposed by conventional methods like mean and mode by suggesting an ANN model for accurate load forecasting. Specifically, the LSTM and random forest approach has been used here. We compared our model to other models that use similar parameters and found that ours is more reliable and can be used for long-term forecasting. Our model has achieved an average overall accuracy of 96% and an average MSE of 4.486 with average CPU time consumption of 904.47 s, 872.43 s, and 908.32 s, respectively. Hence, the present model outperforms other existing methods.