scholarly journals Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model

2021 ◽  
Vol 282 ◽  
pp. 116180
Author(s):  
Joakim Munkhammar ◽  
Dennis van der Meer ◽  
Joakim Widén
Keyword(s):  
Markov Chain ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Mixture Distribution ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Residential Electricity ◽  
Residential Electricity Consumption

scholarly journals Short-Term Load Forecasting of building electricity consumption using NARX Neural Networks model

2021 ◽  
Author(s):  
Irati Zapirain Zuazo ◽  
Zina Boussaada ◽  
Naiara Aginako ◽  
Octavian Curea ◽  
Haritza Camblong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Short Term ◽  
Short Term Load Forecasting

Short-Term Load Forecasting Using Hybrid Neural Network

2021 ◽  
Vol 12 (1) ◽  
pp. 142-156
Author(s):  
Muhammad Nadeem ◽  
Muhammad Altaf ◽  
Ayaz Ahmad
Keyword(s):  
Neural Network ◽  
New England ◽  
Low Cost ◽  
Electrical Power ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Multiple Variables

One of the important factors in generating low cost electrical power is the accurate forecasting of electricity consumption called load forecasting. The major objective of the load forecasting is to trim down the error between actual load and forecasted load. Due to the nonlinear nature of load forecasting and its dependency on multiple variables, the traditional forecasting methods are normally outperformed by artificial intelligence techniques. In this research paper, a robust short term load forecasting technique for one to seven days ahead is introduced based on particle swarm optimization (PSO) and Levenberg Marquardt (LM) neural network forecast model, where the PSO and LM algorithm are used for the training process of neural network. The proposed methods are tested to predict the load of the New England Power Pool region's grid and compared with the existing techniques using mean absolute percentage errors to analyze the performance of the proposed methods. Forecast results confirm that the proposed LM and PSO-based neural network schemes outperformed the existing techniques.

scholarly journals Comparison of Regression and Neural Network Model for Short Term Load Forecasting: A Case Study

2021 ◽  
Vol 12 (1) ◽  
pp. 29
Author(s):  
Javaid Aslam ◽  
Waqas Latif ◽  
Muhammad Wasif ◽  
Iftikhar Hussain ◽  
Saba Javaid
Keyword(s):  
Neural Network ◽  
Power System ◽  
Research Work ◽  
Load Forecasting ◽  
Back Propagation ◽  
Electricity Consumption ◽  
Power System Planning ◽  
Short Term ◽  
System Planning ◽  
Short Term Load Forecasting

Short term load forecasting (STLF) is an obligatory and vibrant part of power system planning and dispatching. It utilized for short and running targets in power system planning. Electricity consumption has nonlinear patterns due to its reliance on factors such as time, weather, geography, culture, and some random and individual events. This research work emphasizes STLF through utilized load profile data from domestic energy meter and forecasts it by Multiple Linear Regression (MLR) and Cascaded Forward Back Propagation Neural Network (CFBP) techniques. First, simple regression statistical calculations used for prediction, later the model improved by using a neural network tool. The performance of both models compared with Mean Absolute Percent Error (MAPE). The MAPE error for MLR observed as 47% and it reduced to 8.9% for CFBP.

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