Modified meta heuristics and improved backpropagation neural network-based electrical load demand prediction technique for smart grid

2017 ◽  
Vol 12 ◽  
pp. S20-S32 ◽  
Author(s):  
Badar Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Smart Grid ◽  
Electrical Load ◽  
Backpropagation Neural Network ◽  
Demand Prediction ◽  
Load Demand ◽  
Prediction Technique

scholarly journals Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

2021 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
Kumilachew Chane ◽  
◽  
Fsaha Mebrahtu Gebru ◽  
Baseem Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Electric Utility ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

A Novel Hybrid Deep Correction Approach for Electrical Load Demand Prediction

2021 ◽  
pp. 103161
Author(s):  
Fuhua Yu ◽  
Qi Yue ◽  
Arda Yunianta ◽  
Hani Moaiteq Abdullah Aljahdali
Keyword(s):  
Electrical Load ◽  
Demand Prediction ◽  
Load Demand

scholarly journals A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid

2020 ◽  
Vol 12 (4) ◽  
pp. 1653 ◽  
Author(s):  
Fatma Yaprakdal ◽  
M. Berkay Yılmaz ◽  
Mustafa Baysal ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Energy Generation ◽  
Electrical Load ◽  
Power Losses ◽  
Short Term ◽  
Real Power ◽  
Load Demand ◽  
Photovoltaic Power ◽  
Renewable Energy Generation

The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.

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