Power Consumption Estimation Using Artificial Neural Networks: The Case of Turkey

A significant proportion of the world energy consumption is by developing countries. As a developing country, Turkey is one of the leading countries in terms of the increase in energy demand. According to the data from the Ministry of Energy and Natural Resources, Turkey is the country with the greatest increase in demand after China in electricity and natural gas consumption since 2000. In 1970, the ratio of total energy production to consumption in Turkey was 76%. In year 2000, this ratio dropped down to 35%, in year 2010 to 26% and predicted to come down to 23% by year 2020. This situation indicates an increase in Turkey’s energy dependency every passing year and the need to implement solutions to reduce this dependency. Today, electric energy has become a very critical and indispensable part of the development of technology. Production and consumption of electrical energy, which facilitates human life and increases labour productivity, are increasing every year. Electricity is a versatile and easily controlled form of energy. Electricity is practically non-existent and non-polluting at the point of use. Electricity can be cleanly produced by completely renewable methods such as wind, water and sunlight at the production point. Electricity market has a unique feature compared to other commodities. This feature requires the consumption of electricity when it is produced. Forecasting the future consumption of electricity in Turkey is crucial in making strategic plans for the future and taking the necessary measures. In Turkey, the consumption of electricity in the estimation studies were generally observed that the use of long-term electricity consumption prediction method of neural networks. In some studies, the results obtained by artificial neural network method are compared with Box-Jenkins models and regression technique. As a result of comparison, artificial neural networks seem to be a good predictor of electricity consumption. In this study, electrical consumption is modelled by using artificial neural network method and the results are discussed. In the application, the four main factors that affect the electricity consumption in Turkey is considered as independent variables. These independent variables are; Population, Imports, Exports, Gross Domestic Product (GDP). How these independent variables affect the electricity consumption in the country was found as the result of the tests made and the results were evaluated

Analysis and applicability of Mersin region wind speed data with artificial neural networks

In this study, wind speed data were analyzed in order to provide energy to the heating and electrical systems of a house with renewable energy sources in Mersin-Mut region. Three-year wind speed data is taken from the Turkey General Directorate of Meteorology in the region. Annual estimation was made with artificial neural networks using 28-day wind speed data for the studied area. Some of the wind data were used for training of the neural network, and some were used for testing. In the artificial neural network model, the most successful model was obtained by changing the number of neurons in the hidden layer. In the analysis made using eight neurons in the hidden layer, the lowest MAE and RMSE error values were calculated. While the number of neurons was eight, MAE and RMSE values were obtained as 0.4056 and 0.5403, respectively. In addition, analysis of wind data with WAsP software has been carried out for this region. Thus, the average instantaneous wind speed was determined according to the analysis studies

Modified Gravitational Search Algorithm for Energy Demand Estimation of Turkey

Estimation of energy demand beforehand is a quite significant problem in respect of economy and sources of country. In this study, Gravitational Search Algorithm (GSA) was modified by making some innovations in GSA and called as Modified Gravitational Search Algorithm (MGSA). Energy demand estimation is conducted through the relationship between the increase in economic indicators in Turkey and energy consumption. Estimation was actualized by using gross domestic product (GSYH), importation, exportation and demography for energy demand estimation and both linear and exponential equations. Energy demand between the years 2017-2037 was predicted by using the data belong to 1997-2011. The years between 2012 and 2016 were used as test data. It was observed that the results acquired via MGSA estimate better compared to GSA results

Energy use and CO2 emissions of the Moroccan transport sector

In this paper, optimized models based on two different machine learning (ML) methods were developed to forecast the transport energy consumption (TEC) and carbon dioxide (CO2) emissions in Morocco by 2030. More precisely, artificial neural networks (ANN) and support vector regression (SVR) were used for modelling non-linear TEC and CO2 emissions data. This study uses data from 1990 to 2020 and employs various independent parameters, including population, gross domestic product, urbanization rate, evolution of the number of vehicles, and the number of electric vehicle introductions. Four statistical metrics are derived to assess the effectiveness of the ML algorithms used. The forecasts for 2030 were based on six scenarios, including three scenarios for the growth of gross domestic product (GDP) and two scenarios for the evolution of electric cars’ introduction into Moroccan vehicle fleet. The ANN model outputs showed that a decrease in TEC and CO2 emissions is expected until 2030. However, the SVR model predicts outputs values close to those in 2020. The study's results also indicate that: i) TEC and transport CO2 emissions are positively impacted by economic growth in Morocco and ii) electric vehicles will be essential components enabling substantial reductions in overall CO2 emissions in future transport systems

Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

Forecasting of Turkey's Sectoral Energy Demand by Using Fuzzy Grey Regression Model

Population growth, technological developments, economical growth and efforts to achieve a high standard of living increase the demand for energy. Satisfying this increasing demand without interruption is of vital importance for countries to ensure security of supply. Safely forecasting the energy demand of Turkey, which is about 3-4 times the world average, is important for sustainable development and improving standards of living in the country. This study seeks to forecast Turkey's total energy demand and determine the distribution of this demand among sectors and the amount of unutilized energy. In the study, the energy demand projection until 2023 was revealed with fuzzy grey regression model (FGRM) using the data between years 1990-2012. Keywords: fuzzy grey prediction; sectorial energy demand in Turkey; fuzzy grey regression model JEL Classifications: C610, L69

Modeling and Forecasting Turkey’s Electricity Consumption by using Artificial Neural Network

The article describes the future projections of electricity demand in Turkey by using multiple linear regression (MLR) and artificial neural Networks (ANN). For this purpose six independent variables which are GDP, population, import, export, employment and natural gas are identified as the possible predictors of electricity consumption. We used MLR to determine which independent variables will be selected to forecast future electricity consumption with ANN. These variables are used in stepwise regression in order to identify which variables predict the dependent variable best by using 1992 - 2014 data. Four different models were identified as the result of MLR including various combinations of selected four variables that are population, import, natural gas and employment. In model 1 population, in model 2 population, import, in model 3 population, import and natural gas, in model 4 population, import, natural gas and employment are used as independent variables in ANN.  In this study to model the proposed problem of forecasting Turkey’s electricity consumption in the years 2015-2023, a feed forward multilayer perceptron neural network has been used. According to the forecasted results of four models Turkey’s electricity consumption is projected to vary between 337087.4 and 385006.6 Gwh by 2023. Forecasted results were compared with Turkish Electricity Transmission Company (TEIAS) projections. Except Model 2 our forecast results showed lower values than TEIAS estimates

Avances recientes en la predicción de la demanda de electricidad usando modelos no lineales

La predicción de la demanda es un problema de gran importancia para el sector eléctrico, ya que a partir de sus resultados, los agentes del mercado de energía toman las decisiones más adecuadas para su labor. En este artículo se presenta un análisis de las técnicas y modelos más usados en el pronóstico de la demanda de electricidad y la problemática o dificultades a las que se enfrentan los investigadores al momento de realizar un pronóstico. El análisis muestra que las técnicas más usadas son los modelos ARIMA y las redes neuronales artificiales. Sin embargo, se encontró poca claridad sobre cuál modelo es más adecuado y en qué casos, adicionalmente, los estudios no presentan una recomendación específica para desarrollar modelos de pronóstico de demanda, específicamente en el caso colombiano. Finalmente, se propone realizar un estudio sistemático con el fi n de determinar los modelos más adecuados para predicción de demanda para el caso colombiano