Analysis and forecasting of nonresidential electricity consumption in Romania

Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio; Minea, Alina Adriana

doi:10.1016/j.apenergy.2010.05.018

Cited by 121 publications

(52 citation statements)

References 24 publications

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“…Bianco et al [11] presented an analysis and two forecast models for nonresidential electricity consumption in Romania. The two models lead to similar results, with an average deviation less than 5 %.…”

Section: Review Of Literaturementioning

confidence: 99%

Energy’s exports forecasting by a neuro-fuzzy controller

2015

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Since accurate forecasting of energy export is very important for planning potential energy demand and improving the energy production sector, various forecasting methods have been developed. The present work is focused to apply a novel technique, an integrated neuro-fuzzy controller named PATSOS. The forecasting system is based on two Adaptive Neural Fuzzy Inference Systems (ANFIS) that form an inverse controller. An ANFIS model represents the controller and another ANFIS represents the energy export model that is going to be controlled. ANFIS uses a combination of the least-squares method and the backpropagation gradient descent method to estimate the optimal energy export forecast parameters. The ANFIS controller belongs to direct control and is based on inverse learning, also known as general learning. Hourly data sets during the period were used to learn and evaluate the proposed system. The forecast accuracy of the proposed technique was evaluated using out of sample tests. The results of the simulation based on statistical errors and the experimental investigations carried out on the laboratory showed that the model despite the high data volatility, is suitable for forecasting hourly energy exports.

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Section: Review Of Literaturementioning

confidence: 99%

Energy’s exports forecasting by a neuro-fuzzy controller

2015

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“…Mohamed and Bodger [22] used multiple linear regression analysis to investigate the influence of GDP, average price of electricity and population on the annual electricity consumption in New Zealand. Bianco et al [23] completed a long-term consumption forecasting up to the year 2020 and examined the relationship between GDP, price elasticities and nonresidential electricity consumption in Romania. Ekonomou [24] investigated the influence of temperature, installed power capacity, GDP, and yearly per resident electricity consumption on the Greek long-term energy consumption prediction.…”

Section: Mid-long Term Electricity Consumption Forecastingmentioning

confidence: 99%

“…It turns out that in practice, a large proportion of potential variables can be adopted for the forecasting. To address the relationship between electricity consumption and its drivers, researchers have investigated various approaches for the mid-long term electricity demand forecasting [16,[18][19][20][21][22][23][24][25][26][27][28][29][30]. Azadeh et al [18] proposed an adaptive network-based fuzzy inference system stochastic frontier analysis adopting two input variables, namely GDP and population to forecast the long-term natural gas consumption.…”

Section: Mid-long Term Electricity Consumption Forecastingmentioning

confidence: 99%

Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting in China

et al. 2015

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“…A. Azadeh and et al, examined the forecasting electrical consumption by integration of neural network, time series and ANOVA [7]. Vincenzo Bianco and et al, expressed and forecasted of nonresidential electricity consumption in Romania [8]. Zaid Mohamed and Pat Bodger analyzed and forecasted electricity consumption in New Zealand using economic and demographic variables (GDP, the average price of electricity and Population) [9].…”

Section: Related Workmentioning

confidence: 99%