Long term distribution demand forecasting using neuro fuzzy computations

Padmakumari, K.; Mohandas, K. P.; Thiruvengadam, S. J.

doi:10.1016/s0142-0615(98)00056-8

Cited by 57 publications

(21 citation statements)

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“…Load forecast has become increasingly important since the rise of the competitive energy markets. Many forecast models have been proposed and implemented in this field including linear regression [1] and econometric models [2], neuro-fuzzy models [3] and data mining procedures [4]. Artificial intelligent techniques have been also applied [5,6], while simple AutoRegressive (AR) [7], AutoRegressive Integrated Moving Average (ARIMA) [8] and AutoRegressive Moving Average (ARMA) [9][10][11][12] models have presented very good forecasted electricity demand load and electricity prices results.…”

Section: Introductionmentioning

confidence: 99%

Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Pappas

Ekonomou

Karamousantas

et al. 2008

Energy

275

104

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Section: Introductionmentioning

confidence: 99%

Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Pappas

Ekonomou

Karamousantas

et al. 2008

Energy

275

104

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“…Forecasting accuracy of ANN depends on learning data set and their adequacy. Moreover ANN methods sometimes get stuck in local minimum, so choosing proper data set, is too critical in neural network models and these models get good results only when the number of data is high [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

Barak

Sadegh

2016

International Journal of Electrical Power & Energy Systems

277

113

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a b s t r a c tEnergy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA-ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA's output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented.The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model's MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done. IntroductionEnergy is vital important for development of every country from the social, economic and environmental perspective. It has magnificent effect on industrial and agricultural products, health, sanitary, population, education and human life quality [1].As energy is a crucial input to industrial part of country, energy demand increases along the industrial function increase. Rapid changes in industry and economy strongly affect energy consumption. Therefore, energy consumption is an important economical index that represents economic development of a city or a country [2]. According to the international energy agent report, there should be many transformations in amount and type of future energy consumption (year 2030). As over the past decade global energy consumption has increased rapidly because of population and economic growth [3,4]. According to wide growth of energy consumption in the last decade, energy demand management is very important for achieving economic success, environment preservation and suitable planning for existing resources that result in self-sufficiency and economic development. Therefore, various techniques have been used for energy demand management to forecast future energy demands accurately [4]. However, energy forecasting is difficult, because it is affected by rapid development of economy, technology, gove...

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“…The initial studies were based on statistical models, using for instance integrated moving average models (ARIMA) (Abdel-Aal and Al-Garni, 1997; Saab et al, 2001) or models based on regression (Mohgram and Rahman, 1989;Papalexopoulos and Hesterberg, 1990). Their low accuracy in time series with non-linear characteristics prompted the application of artificial intelligence techniques, such as neural networks (Papalexopoulos et al, 1994;Chowand and Leung, 1996), hybrids methods (Srinivasan et al, 1999;Padmakumari et al, 1999) or genetic algorithms (Tzafestas and Tzafestas, 2001). Taylor and McSharry (2007) evaluate different short-term load forecasting methods: (i) ARIMA model; (ii) Periodic AR model; (iii) an extension for double seasonality of Holt-Winters exponential smoothing method; (iv) an alternative exponential smoothing method; (v) a method based on the principal component analysis (PCA) of the daily demand profiles, concluding from the results obtained that the double seasonal Holt-Winters exponential smoothing method is the best of these methods.…”

Section: Introductionmentioning

confidence: 99%