Seasonal climate forecasts for medium-term electricity demand forecasting

Felice, Matteo De; Alessandri, Andrea; Catalano, Franco

doi:10.1016/j.apenergy.2014.10.030

Cited by 127 publications

(66 citation statements)

References 18 publications

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“…Although several different forecasting methods are used for prediction of electricity demand, none of them is superior in all cases. Some of these techniques used to forecast electricity demand of countries are the time series model (Saab et al, 2001;Sa'ad, 2009;Dilaver and Hunt, 2011;Boran, 2014;Efendi et al, 2014), artificial neural networks (ANNs) model (Hamzacebi and Kutay, 2004;Hamzacebi, 2007;Azadeh et al, 2008;Cunkas and Altun, 2010;Panklib et al, 2015) , regression and econometric model (Mohamed and Bodger, 2005;Al-Shobaki and Mohsen, 2008;Meng and Niu, 2011;Bildirici et al, 2012;Bianco et al, 2013), neuro-fuzyy model (Demirel et al, 2010;Chang et al, 2011), heuristic optimization method (El-Telbany and ElKarmi, 2008;Cunkas and Taskiran, 2011;Zhu et al, 2011), and support vector regression model (SVR) (De Felice et al, 2015;Jain et al, 2014;Kaytez et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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“…The effect of weather variables on load forecasting in mid-term horizon is extensively studied in refs. [73][74][75]. Autonomous approach has been widely accepted in MTLF modelling, where the historical load and weather data are the main load impacting variables.…”

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

128

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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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“…The CM technique has proved to be successful for the analysis of different climate fields, like precipitation, vegetation characteristics, sea surface temperature (SST), and temperature over land (Alessandri and Navarra, 2008;Cherchi et al, 2007;Wang et al, 2011a). Recently, the CM technique has been also applied to investigate the relationship between surface temperature and electricity demand in summer (De Felice et al, 2014). By taking advantage of the new global array of relevant up-to-date high-quality data sets, the present work substantially extends the analysis previously performed by Alessandri and Navarra (2008) and, for the first time, it includes SM and evapotranspiration (ET) feedbacks on PRE.…”

Section: Introductionmentioning

confidence: 99%

Observationally based analysis of land–atmosphere coupling

et al. 2016

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Abstract. The temporal variance of soil moisture, vegetation and evapotranspiration over land has been recognized to be strongly connected to the temporal variance of precipitation. However, the feedbacks and couplings between these variables are still not well understood and quantified. Furthermore, soil moisture and vegetation processes are associated with a memory and therefore they may have important implications for predictability.In this study we apply a generalized linear method, specifically designed to assess the reciprocal forcing between connected fields, to the latest available observational data sets of global precipitation, evapotranspiration, vegetation and soil moisture content. For the first time a long global observational data set is used to investigate the spatial and temporal land variability and to characterize the relationships and feedbacks between land and precipitation.The variables considered show a significant coupling among each other. The analysis of the response of precipitation to soil moisture evidences a robust coupling between these two variables. In particular, the first two modes of variability in the precipitation forced by soil moisture appear to have a strong link with volcanic eruptions and El Niño-Southern Oscillation (ENSO) cycles, respectively, and these links are modulated by the effects of evapotranspiration and vegetation. It is suggested that vegetation state and soil moisture provide a biophysical memory of ENSO and major volcanic eruptions, revealed through delayed feedbacks on rainfall patterns. The third mode of variability reveals a trend very similar to the trend of the inter-hemispheric contrast in sea surface temperature (SST) and appears to be connected to greening/browning trends of vegetation over the last three decades.

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Seasonal climate forecasts for medium-term electricity demand forecasting

Cited by 127 publications

References 18 publications

Primary energy sources planning based on demand forecasting: The case of Turkey

Primary energy sources planning based on demand forecasting: The case of Turkey

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

Observationally based analysis of land–atmosphere coupling

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