Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

Kumaran, J.; Ravi, G.

doi:10.1080/15325008.2015.1028115

Cited by 26 publications

(18 citation statements)

References 25 publications

(23 reference statements)

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“…The historical data comprising India's primary fuels such as coal, lignite, crude oil and natural gas, the per capita GDP and the population data during the period of 1980-2012, are taken from [2,11,12]. The results of the PM are compared with that of the regression model (RM) [10] with a view to illustrate the superiority of the PM. The accuracy of the forecast is evaluated through the following mean absolute percent error (MAPE) [10].…”

Section: Resultsmentioning

confidence: 99%

“…The results of the PM are compared with that of the regression model (RM) [10] with a view to illustrate the superiority of the PM. The accuracy of the forecast is evaluated through the following mean absolute percent error (MAPE) [10]. The results of the PM along with RM for the chosen yester years are given in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…In the light of the fact that the per capita GDP and population establish a good relationship with the primary fuels' demand of any country in the long-term forecasting model, they are not readily available for the future years [10]. However, they can be forecasted for future years.…”

Section: Proposed Modelmentioning

confidence: 99%

“…The ANNs are popular for pattern mapping or function approximation and frequently preferred for solving forecasting problems. Usually, the multi-layer feed forward networks, comprising of an input layer, an output layer and a hidden layer, each with a set of neurons, are used in forecasting models [10].…”

Section: Proposed Modelmentioning

confidence: 99%

See 3 more Smart Citations

Dragonfly Optimization based ANN Model for Forecasting India's Primary Fuels' Demand

Kumaran¹,

Sasikala²

2017

IJCA

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This paper presents a dragonfly optimization (DFO) based ANN model for predicting India's primary fuel demand. It involves socio-economic indicators such as population and per capita GDP and uses two ANNs, which are trained through DFO algorithm. The method optimizes the connection weights of ANN models through effectively searching the problem space in finding the global best solution. Primary fuel demands during the years 1990-2012 forms the data for training and validating the model. The proposed model requires an input, the year of the forecast, and predicts the primary fuels' demand. The forecasts up to the year 2025 are compared with that of the RM with a view to illustrate the accuracy. General Termsforecasting, primary energy fuels Keywords artificial neural network, dragonfly optimization, regression model.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

See 2 more Smart Citations

Dragonfly Optimization based ANN Model for Forecasting India's Primary Fuels' Demand

Kumaran¹,

Sasikala²

2017

IJCA

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“…[28] Before that, Kermanshahi [91] in 1998 used ANN forecast load for 10 years, Ekonomou [92] used ANN to forecast load in Greece. Other commendable work in LTLF using ANN is reported in literatures [93][94][95][96][97][98][99][100].…”

Section: Long-term Load Forecasting Overviewmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

134

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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COVID-19 Detection via Wavelet Entropy and Biogeography-Based Optimization

Yao

Han

2020

Lecture Notes on Data Engineering and Communications Technologies

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Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

Cited by 26 publications

References 25 publications

Dragonfly Optimization based ANN Model for Forecasting India's Primary Fuels' Demand

Dragonfly Optimization based ANN Model for Forecasting India's Primary Fuels' Demand

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

COVID-19 Detection via Wavelet Entropy and Biogeography-Based Optimization

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