Efficient Electricity Forecasting in Multiple Residential Buildings considering Demand-Side Management

2021 ◽  
pp. 49-63
Author(s):  
Piyush Kumar Shukla ◽  
◽  
Prashant Kumar Shukla ◽  
Keyword(s):  
Deep Learning ◽  
Smart Grid ◽  
Smart Grids ◽  
Residential Buildings ◽  
Load Forecasting ◽  
Time Interval ◽  
Short Time Interval ◽  
Area Network ◽  
Load Prediction ◽  
Home Area

Gateway based Home Area Network (HAN ) to Neighbourhood Area Network (NAN); NAN to HAN improved the communication in the Smart grid. The gateway reduces the Load dispatch centre (LDC) work in varying power consumption in a short time interval. The proposed work explains the working of gateway, reducing the work of LDC using the load scheduling procedure. Deep learning methods incorporated gateway will aid in achieving the requirement. It reduces black start operation and it may be prevented by indulging consumers in the supply automation of the grid. It will produce the grid to maintain the operating frequency, avoiding the substations' disciplinary charges. A variety of types of abrupt load variation and load kinds has been taken in this function. The analysis shows that the gateway achieves a decrease in complexity in the proposed work. This method provides the detail of employment of deep learning for predicting the load forecasting performances in smart grids that can be made better through a gateway between SMs-DCU. The Proposed work compares with systems that employ the predictable profound estimating methods for load forecasting, which has provided better performance. Load Prediction, Deep Learning, Gateway, Smart Grid, Estimation

scholarly journals Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 247
Author(s):  
Maher Selim ◽  
Ryan Zhou ◽  
Wenying Feng ◽  
Peter Quinsey
Keyword(s):  
Deep Learning ◽  
Smart Grid ◽  
Smart Grids ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Gradient Boosting ◽  
Electric Load ◽  
Short Term ◽  
Energy Forecasting ◽  
Uncertainty Estimates

Building safe, reliable, fully automated energy smart grid systems requires a trustworthy electric load forecasting system. Recent work has shown the efficacy of Long Short-Term Memory neural networks in energy load forecasting. However, such predictions do not come with an estimate of uncertainty, which can be dangerous when critical decisions are being made autonomously in energy production and distribution. In this paper, we present methods for evaluating the uncertainty in short-term electrical load predictions for both deep learning and gradient tree boosting. We train Bayesian deep learning and gradient boosting models with real electric load data and show that an uncertainty estimate may be obtained alongside the prediction itself with minimal loss of accuracy. We find that the uncertainty estimates obtained are robust to changes in the input features. This result is an important step in building reliable autonomous smart grids.

scholarly journals Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 54992-55008
Author(s):  
Dabeeruddin Syed ◽  
Haitham Abu-Rub ◽  
Ali Ghrayeb ◽  
Shady S. Refaat ◽  
Mahdi Houchati ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Smart Grid ◽  
Load Forecasting ◽  
Consumption Pattern ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals A Situation-Aware Scheme for Efficient Device Authentication in Smart Grid-Enabled Home Area Networks

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 989 ◽  
Author(s):  
Anhao Xiang ◽  
Jun Zheng
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Communication Complexity ◽  
Situational Awareness ◽  
Security Analysis ◽  
Risk Level ◽  
Security Risk ◽  
Risk Levels ◽  
Home Area ◽  
Home Area Networks

Home area networks (HANs) are the most vulnerable part of smart grids since they are not directly controlled by utilities. Device authentication is one of most important mechanisms to protect the security of smart grid-enabled HANs (SG-HANs). In this paper, we propose a situation-aware scheme for efficient device authentication in SG-HANs. The proposed scheme utilizes the security risk information assessed by the smart home system with a situational awareness feature. A suitable authentication protocol with adequate security protection and computational and communication complexity is then selected based on the assessed security risk level. A protocol design of the proposed scheme considering two security risk levels is presented in the paper. The security of the design is verified by using both formal verification and informal security analysis. Our performance analysis demonstrates that the proposed scheme is efficient in terms of computational and communication costs.

Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids

2020 ◽  
Vol 16 (7) ◽  
pp. 4703-4713 ◽  
Author(s):  
Yandong Yang ◽  
Wei Li ◽  
T. Aaron Gulliver ◽  
Shufang Li
Keyword(s):  
Deep Learning ◽  
Smart Grids ◽  
Load Forecasting ◽  
Probabilistic Load

scholarly journals Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL

2022 ◽  
Vol 2161 (1) ◽  
pp. 012068
Author(s):  
Sthitprajna Mishra ◽  
Bibhu Prasad Ganthia ◽  
Abel Sridharan ◽  
P Rajakumar ◽  
D. Padmapriya ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Smart Grid ◽  
User Interfaces ◽  
Load Forecasting ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Load Prediction ◽  
Artificial Neural ◽  
Optimal Reserve

Abstract The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.

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