Use of Artificial Neural Networks for Short-Term Electricity Load Forecasting of Kenya National Grid Power System

Moturi, Christopher A.; Kioko, Francis K.

doi:10.5120/10439-5123

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…The network usually gets information from the input & output data, using training techniques and transfer functions. [11][27] Back propagation is one of the supervised learning programs that utilize the universal function approximate to make use of a quadratic error function's gradient descent. The gradient descent methodology is used during the learning phase to reduce total error of the network's results and output generated.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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

Mishra

Ganthia

Sridharan

et al. 2022

J. Phys.: Conf. Ser.

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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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Section: Artificial Neural Networkmentioning

confidence: 99%

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

Mishra

Ganthia

Sridharan

et al. 2022

J. Phys.: Conf. Ser.

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“…The proposed model was used to forecast electricity demand from the half-hour forecast to seven days for Australian National Electricity Market power systems [7]. Moturi and Kioko developed a model based on artificial neural network for short-term electric load forecasting, and evaluated the performance of the model to load forecasting a day ahead using actual load data from the National Electric Transmission Company of Kenya [8]. Senjyu et al with an adaptive learning algorithm developed with artificial neural networks, and investigated the effect of temperature variation on load forecasting during the day.…”

Section: Introductionmentioning

confidence: 99%

Short-term Electric Energy Load Forecasting of Ankara Region Using Artificial İntelligence Methods

AKMAN>¹,

Yılmaz²,

Sönmez³

2023

Politeknik Dergisi

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While energy, which is an indispensable part of everyday life, maintains its importance and place in socio-economic structures of countries, importance of electricity energy, as an important component of energy, is increasing its gravity exponentially. Importance of electricity energy demand maintains its increasing trend in line with growing population, urbanization, industrialization, becoming widespread of technology and welfare. While meeting electricity energy demand, correct and effective load forecasting is needed in order to ensure operational safety without losing the stability of voltage, frequency and power flows within the limits determined under the real time conditions of electric power systems, operate and plan secure and low-cost electricity transmission systems and supply sustainable, reliable, high quality and affordable electricity to consumers. In recent years, use of the artificial intelligence models are quite common in many areas of the power system analysis and control, static and dynamic security analysis, dynamic load modelling and alarm processing and error diagnostics. Therefore, in this study, hybrid artificial neural network model optimized with genetic algorithm has been proposed to perform short-term load forecast since fairly good forecasting results are being obtained especially for non-linear complex problems. The proposed short-term load forecasting model has been tested by making 24 hours load forecasting using actual load data of Ankara Region in Turkey. The results obtained from the proposed model have been compared with classical ANN. In this study, for short-term electric load forecasting based on artificial neural networks and genetic algorithm an adaptive hybrid system has been proposed. Using artificial neural network based on Genetic Algorithm (GA) a new approach for short-term electric load forecasting has been developed. This proposed hybrid algorithm has been implemented for short-term load forecasting. In the proposed model, the actual forecast was made by ANN, Genetic Algorithm is used to select the most appropriate activation function for each node of ANN. Thus, it has been observed that network error decreased even more than classical ANN. The results show that hybrid ANN gives better result than classical ANN. So, the accuracy of the proposed model for short-term load forecasting has been increased. These results showed that the proposed model has a higher accuracy rate than the classical ANN in the short-term load forecasting.

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“…The two variables on which the economic prosperity of developing countries like India is constantly dependent are the reliability and quality of electric power supply [1]. Load forecast is a technique for predicting future loads for a power system.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Optimized Load Forecasting of Smartgrid using MATLAB

Ganthia,

Mohanty,

Mohapatra

et al. 2023

Control Syst. Optim. Lett.

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The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners in making important decisions on unit commitments, energy trading, system security reliability, and optimal reserve capacity. The objective is to produce a desktop version of a personal computer-based complete expert system that 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 a study of load forecasting in smart grids, a detailed study of the architecture and configuration of Artificial Neural Network (ANN), Mathematical modeling and implementation of ANN using MATLAB, and a detailed study of load forecasting using the backpropagation algorithm.

show abstract

Use of Artificial Neural Networks for Short-Term Electricity Load Forecasting of Kenya National Grid Power System

Cited by 12 publications

References 8 publications

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

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

Short-term Electric Energy Load Forecasting of Ankara Region Using Artificial İntelligence Methods

Artificial Neural Network Optimized Load Forecasting of Smartgrid using MATLAB

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