Hybrid adaptive techniques for electric-load forecast using ANN and ARIMA

Desouky, A.A. El; Elkateb, M.M.

doi:10.1049/ip-gtd:20000521

Cited by 112 publications

(53 citation statements)

References 5 publications

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“…It is an adaptive network. An adaptive network is a network structure consisting of a number of nodes connected by directional links [15]. Each node represents a process unit and the links specifying the relationship between the nodes.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Short Term Electric Load Forecasting of 132/33KV Maiduguri Transmission Substation using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Harrison¹,

Dan'isa²,

A.³

et al. 2014

IJCA

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This article provides a way of accurately predicting one-hourahead load of a utility company located in the North Eastern region of Nigeria based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The inputs to the ANFIS are the next-hour temperature, next-hour humidity, day of the week, hour of the day, and the current-hour load. The output is the next-hour load of the entire system. All the data used span the period 2009 to 2012 (4 years). These parameters are non-linear, stochastic (random) and uncertain in nature. Adaptive Neurofuzzy based Inference System (ANFIS), an integrated system, comprising of fuzzy logic and Neural Network was used to model the next hour load, because it can address and solve problems related to non-linearity, randomness and uncertainty of data. 75% of the data was used for training and 25% for checking. From the analysis carried out on the ANFIS-based model; Mean absolute percentage error (MAPE) for a typical Monday, Wednesday and Friday was found to be 12.61%, 12.76% and 12.12%. The Mean absolute error (MAPE) on the entire test data was 24.76%. The analysis shows satisfactory level of accuracy with regards to the ANFIS-based model developed in forecasting the next hour load especially with a correlation (r) value of 84.64%. General TermsLoad Forecasting.

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Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Short Term Electric Load Forecasting of 132/33KV Maiduguri Transmission Substation using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Harrison¹,

Dan'isa²,

A.³

et al. 2014

IJCA

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“…One way is to set the weights explicitly, using a priori knowledge. Another way is to 'train' the neural network by feeding it teaching patterns and letting it change its weights according to some learning rule (Desouky and Elkateb, 2000). The learning situations can be categorized in two distinct sorts: Supervised Learning or Associative Learning in which the network is trained by providing it with input and matching output patterns; Unsupervised Learning or Self-Organization in which an output unit is trained to respond to clusters of pattern within the input and discover statistically salient features of the input population.…”

Section: Training Of Artificial Neural Networkmentioning

confidence: 99%

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

Moturi¹,

Kioko²

2013

IJCA

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This paper developed a supervised Artificial Neural Networkbased model for Short-Term Electricity Load Forecasting, and evaluated the performance of the model by applying the actual load data of the Kenya National Grid power system to predict the load of one day in advance. Raw data was collected, cleaned and loaded onto the model. The model was trained under the WEKA environment and predicted the total load for Kenya National Grid power system. The test results showed that the hour-by-hour approach is more suitable and efficient for a day-ahead load forecasting. Forecast results demonstrated that the model performed remarkably well with increased number of iterations. The result suggests that incremental training approach of a neural network model should be implemented for online testing application to acquire a universal final view on its applicability. General TermsLoad Forecasting.

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“…In the early stage of STLF, many forecasting methods based on mathematical statistics theory are put forward [4,5]. But these methods are not suitable for the prediction of dynamic load time series.…”

Section: Introductionmentioning

confidence: 99%

Short‐Term Load Forecasting Model Based on the Fusion of PSRT and QCNN

Zhang¹

2017

Mathematical Problems in Engineering

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Short-term load forecasting (STLF) model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) was proposed. The quantum computation and chaotic mechanism were integrated into QCNN, which was composed of quantum neurons and chaotic neurons. QCNN has four layers, and they are the input layer, the first hidden layer of quantum hidden nodes, the second hidden layer of chaotic hidden nodes, and the output layer. The theoretical basis of constructing QCNN is Phase Space Reconstruction Theory (PSRT). Through the actual example simulation, the simulation results show that proposed model has good forecasting precision and stability.

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Hybrid adaptive techniques for electric-load forecast using ANN and ARIMA

Cited by 112 publications

References 5 publications

Short Term Electric Load Forecasting of 132/33KV Maiduguri Transmission Substation using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Short Term Electric Load Forecasting of 132/33KV Maiduguri Transmission Substation using Adaptive Neuro-Fuzzy Inference System (ANFIS)

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

Short‐Term Load Forecasting Model Based on the Fusion of PSRT and QCNN

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