A non-linear multivariable regression model for midterm energy forecasting of power systems

Tsekouras, George J.; Dialynas, E.N.; Hatziargyriou, Nikos D.; Kavatza, S.D.

doi:10.1016/j.epsr.2006.11.003

Cited by 71 publications

(36 citation statements)

References 46 publications

(54 reference statements)

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“…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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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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“…It has been widely used in the forecasting field and has a high degree of accuracy. The non-linear multivariable regression method [33][34][35] has achieved better predictive effect than classical ones. It has been applied for midterm load forecasting in the Greek power system.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

Yang

2018

Sustainability

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Construction of new coal-fired power plants in China has posed a huge challenge to energy sustainability. Forecasting the installed capacity more accurately can serve to develop better energy sustainability strategy. A comparison between linear and non-linear forecasting models can more comprehensively describe the characteristics of the prediction data and provide multi-angle analysis of the prediction results. In this paper, we develop four time-series forecasting techniques-metabolism grey model (MGM), autoregressive integrated moving average (ARIMA), grey model (GM)-ARIAM, and nonlinear metabolism grey model (NMGM)-for better forecasting of coal-fired power installed capacity. The average relative errors between the simulation and actual data of the MGM, GM-ARIMA, ARIMA, and NMGM model are 3.37%, 2.13%, 3.71% and 2.36% respectively, which indicate those four models can produce highly accurate results. The forecasting results show the average annual growth rate of China's coal-fired power installed capacity in the next ten years (2017-2016) will be 5.26% a year, which is slower than the average annual growth rate (8.20% a year) for 2007-2016. However, the average annual new added installed capacity for 2017-2026 will be 74 gigawatts, which is higher than the average annual added installed capacity (56 gigawatts) for 2007-2016.

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“…Other works on regression-based method were reported in refs. [77,[79][80][81]. AI-based methods have been widely used for MTLF, mostly ANN [30,32,71,78,[82][83][84].…”

Section: Mid-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

120

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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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A non-linear multivariable regression model for midterm energy forecasting of power systems

Cited by 71 publications

References 46 publications

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

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

Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

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

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