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

Chane, Kumilachew; Gebru, Fsaha Mebrahtu; Khan, Baseem

doi:10.54060/jieee/002.01.002

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…The raw data must be pre-processed before being transformed in order for the model to effectively learn the input-output relationship. The pre-processing operations include normalization, ranking, and correlation (Chane et al, 2021). In order to minimize operational costs, load forecasting models use weather forecasts and other elements to predict the future load (Chane et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…The pre-processing operations include normalization, ranking, and correlation (Chane et al, 2021). In order to minimize operational costs, load forecasting models use weather forecasts and other elements to predict the future load (Chane et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…In short-term load forecasting, time plays a critical role since it has the greatest impact on the customer's load demand. Time indicators used in this study include the date, weekday, and time (Chane et al, 2021).…”

Section: Time Indicatormentioning

confidence: 99%

See 2 more Smart Citations

Regression Model-Based Short-Term Load Forecasting for Load Despatch Centre

Gochhait

Sharma²

2023

JAETS

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Forecasting load is an integral part of the planning, operation, and control of power systems. This paper is part of a research effort aimed at developing better energy demand forecasting models for load dispatch centers (LDCs) in Indian states as part of an ambitious project utilizing artificial intelligence-based load forecasting models. In this paper, we present a half hourly load forecasting method for the energy management system of the project that will be used at 33 /11 kV and 0.415 kV substations with good accuracy. The paper uses the half-hourly load consumption dataset collected from MSEDCL for Maharashtra from July 1, 2020 through August 31, 2022. This paper evaluates 24 regression model-based half hourly based load forecasting algorithms for ALE PHATA load based on the load consumption dataset and the collected meteorological dataset. The 24 models in MATLAB Regression belong to five types of regression models: Linear Regression, Regression Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), Ensemble of Trees, and Neural Networks. As a consequence of their nonparametric kernel-based probabilistic nature, the GPR family of models demonstrates the best load forecasting performance. Least squares estimation was used to determine the regression coefficients. There is a direct correlation between load in an electrical power system and temperature, due point, and seasons, as well as a correlation between load and previous load consumption. Therefore, the input variables are Wet Bulb Temperature at 2 Meters (C), Dew/Frost Point at 2 Meters (C), Temperature at 2 Meters (C), Relative Humidity at 2 Meters (%), Specific Humidity at 2 Meters (g/kg) and Wind Speed at 10 Meters (m/s). The mean absolute percentage error and the R squared are used to validate or verify the accuracy of the model, which is shown in the results section. Based on this study, two GPR models are recommended for load forecasting, the Rational Quadratic GPR and the Exponential GPR and Exponential GPR as final model.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Regression Model-Based Short-Term Load Forecasting for Load Despatch Centre

Gochhait

Sharma²

2023

JAETS

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A review of hybrid renewable energies optimisation: design, methodologies, and criteria

Ajiboye,

Ochiegbu,

Ofosu

et al. 2023

International Journal of Sustainable Energy

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Rainfall Analysis and Forecasting Using Deep Learning Technique

Kanchan¹,

Shardoor²

2021

J. Infor. Electr. Electron. Eng.

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Rainfall forecasting is very challenging due to its uncertain nature and dynamic climate change. It's always been a challenging task for meteorologists. In various papers for rainfall prediction, different Data Mining and Machine Learning (ML) techniques have been used. These techniques show better predictive accuracy. A deep learning approach has been used in this study to analyze the rainfall data of the Karnataka Subdivision. Three deep learning methods have been used for prediction such as Artificial Neural Network (ANN) - Feed Forward Neural Network, Simple Recurrent Neural Network (RNN), and the Long Short-Term Memory (LSTM) optimized RNN Technique. In this paper, a comparative study of these three techniques for monthly rainfall prediction has been given and the prediction performance of these three techniques has been evaluated using the Mean Absolute Percentage Error (MAPE%) and a Root Mean Squared Error (RMSE%). The results show that the LSTM Model shows better performance as compared to ANN and RNN for Prediction. The LSTM model shows better performance with mini-mum Mean Absolute Percentage Error (MAPE%) and Root Mean Squared Error (RMSE%).

show abstract

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

Cited by 3 publications

References 36 publications

Regression Model-Based Short-Term Load Forecasting for Load Despatch Centre

Regression Model-Based Short-Term Load Forecasting for Load Despatch Centre

A review of hybrid renewable energies optimisation: design, methodologies, and criteria

Rainfall Analysis and Forecasting Using Deep Learning Technique

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