# Computational Modeling of Electricity Consumption Using Econometric Variables Based on Neural Network Training Algorithms

T. M. Usha1 , S. Appavu alias Balamurugan8

Estimated H-index: 1

Estimated H-index: 8

T. M. Usha1 , S. Appavu alias Balamurugan8

Estimated H-index: 1

Estimated H-index: 8

📖 Papers frequently viewed together

Predicting electricity consumption: A comparative analysis of the accuracy of various computational techniques

2015CITA: Conference on Information Technology in Asia

3 Authors (Patrick Ozoh, ..., Jane Labadin)

References32

Gradient Descent with Momentum Based Backpropagation Neural Network for Selection of Industrial Robot

#1Sasmita Nayak (Government College)H-Index: 1

#2B.B. Choudhury (Indira Gandhi Institute of Technology)H-Index: 6

Last. Saroj Kumar LenkaH-Index: 13

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Fast development of industrial robots and its utilization by the manufacturing industries for many different applications is a critical task for the selection of robots. As a consequence, the selection process of the robot becomes very much complicated for the potential users because they have an extensive set of parameters of the available robots. In this paper, gradient descent momentum optimization algorithm is used with backpropagation neural network prediction technique for the selection of...

#1Hamed Chitsaz (U of C: University of Calgary)H-Index: 6

#2Hamid Shaker (U of C: University of Calgary)H-Index: 7

Last. Nima Amjady (Semnan University)H-Index: 58

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Abstract Electricity load forecasting plays a key role in operation of power systems. Since the penetration of distributed and renewable generation is increasingly growing in many countries, Short-Term Load Forecast (STLF) of micro-grids is also becoming an important task. A precise STLF of the micro-grid can enhance the management of its renewable and conventional resources and improve the economics of energy trade with electricity markets. As a consequence of the highly non-smooth and volatile...

Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines

#1Fazil Kaytez (Gazi University)H-Index: 1

#2M. Cengiz Taplamacioglu (Gazi University)H-Index: 8

Last. Fırat Hardalaç (Gazi University)H-Index: 14

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Accurate electricity consumption forecast has primary importance in the energy planning of the developing countries. During the last decade several new techniques are being used for electricity consumption planning to accurately predict the future electricity consumption needs. Support vector machines (SVMs) and least squares support vector machines (LS-SVMs) are new techniques being adopted for energy consumption forecasting. In this study, the LS-SVM is implemented for the prediction of electr...

Examining performance of aggregation algorithms for neural network-based electricity demand forecasting

#1Saima Hassan (Kohat University of Science and Technology)H-Index: 5

#2Abbas Khosravi (Deakin University)H-Index: 39

Last. Jafreezal Jaafar (UTP: Universiti Teknologi Petronas)H-Index: 12

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Abstract The aim of this research is to examine the efficiency of different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from NN models are combined by three different aggregation algorithms. These aggregation algorithms comprise of a simple average, trimmed mean, and a Bayesian model averaging. These methods are utilized with ce...

#1R. Murugadoss (SIST: Sathyabama University)H-Index: 1

#2M. Ramakrishnan (Velammal Engineering College)H-Index: 1

In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set of affine functional can uniformly approximate any continuous function of n real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of single bidden layer neural networks. In particular, we show that arbitrary decision regions can be arbitrarily well ap...

A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings

#1Luis HernandezH-Index: 18

#2Carlos Baladrón (University of Valladolid)H-Index: 16

Last. Joaquim Massana (University of Girona)H-Index: 7

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Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the `70...

Jun 9, 2013 in ICAISC (International Conference on Artificial Intelligence and Soft Computing)

#1Grzegorz Dudek (Częstochowa University of Technology)H-Index: 16

In the article a simple neural model with local learning for forecasting time series with multiple seasonal cycles is presented. This model uses patterns of the time series seasonal cycles: input ones representing cycles preceding the forecast moment and forecast ones representing the forecasted cycles. Patterns simplify the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and many seasonal cycles. The artificial neural network learns using th...

#1Wen Shen (Masdar Institute of Science and Technology)H-Index: 10

#2Vahan Babushkin (Masdar Institute of Science and Technology)H-Index: 5

Last. Wei Lee Woon (Masdar Institute of Science and Technology)H-Index: 18

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In this work, we try to solve the problem of day-ahead prediction of electricity demand using an ensemble forecasting model. Based on the Pattern Sequence Similarity (PSF) algorithm, we implemented five forecasting models using different clustering techniques: K-means model (as in original PSF), Self-Organizing Map model, Hierarchical Clustering model, K-medoids model, and Fuzzy C-means model. By incorporating these five models, we then proposed an ensemble model, named Pattern Forecasting Ensem...

#1Andrei Marinescu (Trinity College, Dublin)H-Index: 9

#2Colin Harris (Trinity College, Dublin)H-Index: 10

Last. Vinny Cahill (Trinity College, Dublin)H-Index: 34

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Applications such as generator scheduling, household smart device scheduling, transmission line overload management and microgrid islanding autonomy all play key roles in the smart grid ecosystem. Management of these applications could benefit from short-term load prediction, which has been successfully achieved on large-scale systems such as national grids. However, the scale of the data for analysis is much smaller, similar to the load of a single transformer, making prediction difficult. This...

A novel method of short-term load forecasting based on multiwavelet transform and multiple neural networks

#1Zhigang Liu (Southwest Jiaotong University)H-Index: 56

#2Wenfan Li (Southwest Jiaotong University)H-Index: 1

Last. Wanlu Sun (Southwest Jiaotong University)H-Index: 2

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This paper aims to develop a load forecasting method for short-term load forecasting based on multiwavelet transform and multiple neural networks. Firstly, a variable weight combination load forecasting model for power load is proposed and discussed. Secondly, the training data are extracted from power load data through multiwavelet transform. Lastly, the obtained data are trained through a variable weight combination model. BP network, RBF network and wavelet neural network are adopted as the t...

Cited By1

#1Biljana PetkovićH-Index: 2

#2Boris KuzmanH-Index: 6

Last. Miljana BarjaktarevićH-Index: 1

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Economic development could be presented by gross domestic product to show how different factors affect the development. Gross domestic product could be affected by different nonlinear factors in positive or negative way. Hence it is suitable to apply artificial intelligence techniques in order to track the gross domestic product variation in depend on the factors. AI techniques require only input and output data pairs in order to catch the output variations based on the input factors. Therefore ...