Hybrid artificial neural network system for short-term load forecasting

This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF). The system comprises of two Artificial Neural Networks (ANN), assembled in a hierarchical order. The first ANN is a Multilayer Perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor (HLP) for a forecasting day. By using a separate ANN that predicts the integral of the load (ILP), additional information is presented to the actual forecasting ANN (HLP), while keeping its input space relatively small. This property enables online training and adaptation, as new data become available, because of the short training time. Different sizes of training sets have been tested, and the optimum of 30 day sliding time-window has been determined. The system has been verified on recorded data from Serbian electrical utility company. The results demonstrate better efficiency of the proposed method in comparison to non-hybrid methods because it produces better forecasts and yields smaller mean average percentage error (MAPE).

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Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Pertanika Journal of Science and Technology ◽

10.47836/pjst.29.2.14 ◽

2021 ◽

Vol 29 (2) ◽

Author(s):

Oladimeji Ibrahim ◽

Waheed Olaide Owonikoko ◽

Abubakar Abdulkarim ◽

Abdulrahman Okino Otuoze ◽

Mubarak Akorede Afolayan ◽

...

Keyword(s):

Fuzzy Logic ◽

Membership Function ◽

Load Forecasting ◽

Percentage Error ◽

Short Term ◽

Load Forecast ◽

Utility Company ◽

Load Demand ◽

Short Term Load Forecasting ◽

Fuzzy Logic Technique

A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

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Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2213111607666191111095329 ◽

2020 ◽

Vol 13 (7) ◽

pp. 980-992 ◽

Cited By ~ 1

Author(s):

Muhammad F. Tahir ◽

Chen Haoyong ◽

Kashif Mehmood ◽

Noman A. Larik ◽

Asad Khan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Load Forecasting ◽

Vital Role ◽

Short Term ◽

Training Time ◽

Generalization Ability ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Bagging Method

Background: Short Term Load Forecasting (STLF) can predict load from several minutes to week plays a vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. Methods: This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, make this algorithm unable to accurately predict future loads. Results: This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which help in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. Conclusion: This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperforms the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.

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Daily Peak Load Forecast Using Artificial Neural Network

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2256-2263 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2256

Author(s):

Ramesh Kumar V ◽

Pradipkumar Dixit

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Learning Algorithm ◽

Peak Load ◽

Load Forecasting ◽

Percentage Error ◽

Ann Model ◽

Short Term ◽

Short Term Load Forecasting ◽

Artificial Neural

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

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

Journal of Informatics Electrical and Electronics Engineering (JIEEE) ◽

10.54060/jieee/002.01.002 ◽

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.

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