Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025

By Alper Ünler. 2008

221 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/c0bb58be6ae4f7b89662e6faa76a18935e062907

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In Electrical and Electronic Engineering

In Management Science and Operations Research

In Renewable Energy, Sustainability and the Environment

Citations in the corpus

Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines (2015, methodology): “By using particle swarm optimization (PSO); Unler [18] has carried out the long-term demand forecast of Turkey up to the year 2025.”
Energy Access Scenarios to 2030 for the Power Sector in Sub-Saharan Africa (2012, unknown): “13 See e.g., Abraham and Nath, 2001; Adams and Shachmurove, 2008; Basu et al., 1991; Ediger and Tatlidil, 2002; García-Ascanio and Maté, 2010; Pappas et al., 2008; Price and Sharp, 1985 and Ünler, 2008.”
Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand est (2010, unknown): “indicators in Turkey [5].”
Machine learning in energy economics and finance: A review (2019, background): “PSO does not allow for “mutation” as in GA. Ünler (2008) can be considered a seminal energy economics paper proposing a model based on PSO to forecast the energy demand of Turkey.”
Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables (2011, methodology): “However, the energy demand and consumption forecasts determined by MAED still overestimate demand [36].”
Natural gas demand in Turkey (2009, methodology): “A summary of techniques used so far for Turkish energy demand forecasting is given in Table 1 [28].”
Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm (2016, methodology): “It is clear from the related literature [57-59] that usually 4 independent variables, including: population, gross domestic production, import and export, are used as inputs to the models in energy consumption forecasting.”