Electricity demand forecast of Turkey based on hydropower and windpower potential

2016 ◽  
Vol 12 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Aydın Hacı Dönmez ◽  
Yakup Karakoyun ◽  
Zehra Yumurtaci
Keyword(s):  
Electricity Demand ◽  
Demand Forecast

Long-Term Electricity Demand Forecast Using Multivariate Regression and End-Use Method: A Study Case of Maluku-Papua Electricity System

2021 ◽  
Author(s):  
Tumiran Tumiran ◽  
Sarjiya Sarjiya ◽  
Lesnanto Multa Putranto ◽  
Edwin Nugraha Putra ◽  
Rizki Firmansyah Setya Budi ◽  
...  
Keyword(s):  
Multivariate Regression ◽  
Electricity Demand ◽  
Demand Forecast ◽  
Electricity System ◽  
Study Case ◽  
End Use

scholarly journals Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8612
Author(s):  
Santanu Kumar Dash ◽  
Michele Roccotelli ◽  
Rasmi Ranjan Khansama ◽  
Maria Pia Fanti ◽  
Agostino Marcello Mangini
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Sudden Change ◽  
Electricity Demand ◽  
Gradient Boosting ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Daily Consumption ◽  
Energy Provider

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.

scholarly journals An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ping Jiang ◽  
Qingping Zhou ◽  
Haiyan Jiang ◽  
Yao Dong
Keyword(s):  
Search Algorithm ◽  
Arima Model ◽  
Electricity Consumption ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Electricity Demand ◽  
Grey Model ◽  
Grey Theory ◽  
Demand Forecast ◽  
Term Selection

With rapid economic growth, electricity demand is clearly increasing. It is difficult to store electricity for future use; thus, the electricity demand forecast, especially the electricity consumption forecast, is crucial for planning and operating a power system. Due to various unstable factors, it is challenging to forecast electricity consumption. Therefore, it is necessary to establish new models for accurate forecasts. This study proposes a hybrid model, which includes data selection, an abnormality analysis, a feasibility test, and an optimized grey model to forecast electricity consumption. First, the original electricity consumption data are selected to construct different schemes (Scheme 1: short-term selection and Scheme 2: long-term selection); next, the iterative algorithm (IA) and cuckoo search algorithm (CS) are employed to select the best parameter of GM(1,1). The forecasted day is then divided into several smooth parts because the grey model is highly accurate in the smooth rise and drop phases; thus, the best scheme for each part is determined using the grey correlation coefficient. Finally, the experimental results indicate that the GM(1,1) optimized using CS has the highest forecasting accuracy compared with the GM(1,1) and the GM(1,1) optimized using the IA and the autoregressive integrated moving average (ARIMA) model.

scholarly journals PRONÓSTICO EFICIENTE DE LA DEMANDA DIARIA DEL SISTEMA ELECTRICO INTERCONECTADO DEL PERU MEDIANTE ANALISIS ESTOCASTICO ARIMA CON SUCESOS EXTERNOS

2017 ◽  
Vol 24 (1) ◽  
pp. 87
Author(s):  
Salome Gonzales Chávez
Keyword(s):  
Time Series ◽  
Electricity Demand ◽  
Demand Forecast ◽  
External Effects ◽  
Interconnected System ◽  
Palabras Clave ◽  
Day Of The Week ◽  
The Mean ◽  
External Events ◽  
Central South

La demanda diaria del Sistema Eléctrico Interconectado Nacional-SEIN, posee características muy peculiares de tendencia, estacionalidad y aleatoriedad, situación que complica al proceso de estimación de su pronóstico. El objetivo del presente trabajo consiste en formular y calcular modelos ARIMA con Análisis de Sucesos Externos, a fin de lograr pronósticos eficientes de la demanda eléctrica de cada día siguiente, a nivel total y desagregado por áreas. Un buen pronóstico de la demanda diaria garantiza el despecho eficiente y económico de generación y transmisión, así como el aseguramiento y calidad de la demanda sectorial nacional. El enfoque metodológico lo constituye el tratamiento de cada serie temporal objetivo, mediante transformaciones estadístico-matemáticas apropiadas para alcanzar estabilidad tanto en varianzas como en medias regulares y estacionales; paralelamente filtrar los sucesos externos hasta alcanzar a un Modelo ARIMA predictivo de cada área del sistema eléctrico del Perú (Centro, Sur y Norte) y para cada día de la semana. Los resultados alcanzados en la presente investigación demuestran la eficiencia predictiva comparativa. Es decir, tomando como indicador de calidad de pronóstico al Error Absoluto Promedio Porcentual (MAPE), se han obtenido valores inferiores al 1% en las proyecciones de la demanda diaria total del SEIN, frente al 2% que se logra con actuales técnicas determinísticas. Palabras clave.- Pronóstico de Demanda, Despacho Eléctrico, ARIMA, Sucesos Externos, Serie Temporal, Proceso Estocástico, MAPE, Sistema Interconectado Nacional. ABSTRACTThe daily electric demand in Peruvian National Interconnected System-SEIN- has very particular trend, seasonality and characteristics external effects, a situation that complicates the process of estimating the short-term forecast. The aim of this paper is to formulate and calculate ARIMA models with External Events Analysis to achieve efficient forecasts of electricity demand each day, at total level and broken down by areas of the SEIN. The methodology is based on treating each time series using appropriate statistical-mathematical transformations to achieve stability in variance as regular seasonal averages, parallel external events to try to reach an optimal predictive model ARIMA each area of the electrical system of Peru (Central, South and North) and for each day of the week. The results demonstrate the predictive efficiency. Taking as a quality indicator forecast the Mean Absolute Percent Error (MAPE), have obtained values lower than 1% by the projections of the total daily demand SEIN versus 2% obtained with existing deterministic techniques. Keywords.- Demand Forecast, Electricity Demand, ARIMA, External Events, Time Series, Stochastic Process, MAPE, Electric Grid System.

scholarly journals Forecasting electricity demand in South Africa: A critique of Eskom’s projections

2010 ◽  
Vol 106 (1/2) ◽  
Author(s):  
Roula Inglesi ◽  
Anastassios Pouris
Keyword(s):  
South Africa ◽  
Electricity Demand ◽  
Price Determination ◽  
Demand Forecast ◽  
Electricity Crisis ◽  
Short Period ◽  
New Ideas ◽  
Demand Scenario ◽  
The Impact ◽  
Current Energy

Within a short period, Eskom has applied to the National Energy Regulator of South Africa (NERSA) for the third time since the 2008 electricity crisis, proposing a multiyear price determination for the periods 2010−2011 and 2012−2013. The new application, submitted at the end of September 2009, motivated for the debate of strategies with which the consequences of the proposed price hikes could be predicted, measured and controlled. In his presentation to Parliament in February 2009, Eskom’s then CEO, Mr Jacob Maroga presented the current energy situation in the country, the reasons for the crisis in 2007−2008, as well as the challenges of the future. The purpose of this paper is to contribute some new ideas and perspectives to Eskom’s existing arguments regarding the demand for electricity. The most important issue is the fact that Eskom does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. This study proposed that prices have a high impact on the demand for electricity (price elasticity of -0.5). Employing similar assumptions for the country’s economic growth as Eskom, the results of the forecasting exercise indicated a substantial decrease in demand (scenario 1: -31% in 2025 and scenario 2:-18% in 2025). This study’s findings contrasted significantly with Eskom’s projection, which has extensive implications as far as policy is concerned.

Long-term electricity demand forecast and supply side scenarios for Pakistan (2015–2050): A LEAP model application for policy analysis

Energy ◽  
2018 ◽  
Vol 165 ◽  
pp. 512-526 ◽  
Author(s):  
Nayyar Hussain Mirjat ◽  
Muhammad Aslam Uqaili ◽  
Khanji Harijan ◽  
Gordhan Das Walasai ◽  
Md Alam Hossain Mondal ◽  
...  
Keyword(s):  
Policy Analysis ◽  
Electricity Demand ◽  
Supply Side ◽  
Demand Forecast ◽  
Model Application

scholarly journals Demand Simulation for Water, Food Irrigation, and Energy from Micro Hydro Power Plant in Sungai Bayang, Bayang Utara, Pesisir Selatan West Sumatra

2018 ◽  
Vol 43 ◽  
pp. 01015
Author(s):  
Siti Nurlaila Indriani ◽  
Ahmad Agus Setiawan ◽  
Rachmawan Budiarto
Keyword(s):  
Climate Change ◽  
Power Plant ◽  
Good Governance ◽  
Electricity Demand ◽  
Paddy Fields ◽  
Electricity Production ◽  
Climate Change Scenarios ◽  
Demand Forecast ◽  
Hydro Power ◽  
Planning Software

Clean water or fresh water, food and energy are basic human needs. The three basic needs are dependent to one another. The relationship between the three is called the "The nexus of Water, Energy, and Food". It requires good governance on watershed which will be implemented for example to manage water resources to fulfil demand of clean or drinking water, irrigation in food area and energy sources in hydro power plant. This study conducted analysis and simulation to prepare projection of electricity produced by Micro hydro Power Plant (MHP) It integrates a climate change scenarios to forecast its influence on electricity demand and response of river. In addition, the study also presented projections of influence on irrigated food production scenario in irrigation for rice paddy fields. Projection of The MHP electricity and the water demand including for the food sector is conducted by using the WEAP (Water Evaluation and Planning) software, while electricity demand forecast is conducted by applying the LEAP (Long-Range Energy Alternatives Planning) software. The case studies in this study conducted in river flows Bayang’s River. On the river there are three operating MHPs: The Muaro Aie MHP (ity30 kW of installed capac), The Koto Ranah MHP (30 kW) and The Pancuang Taba MHP (40 kW). The LEAP simulation projected electricity demand for Pesisir Selatan until 2025. Demand for South Pesisir Regency up to 2025 is predicted to reach 226.4 GWh with growth of 11.2% per year in BAU scenario, while reach 113.7 GWh with a 5% annual growth in efficiency scenario. The WEAP provided projected electricity production of MHP, basic water needs and irrigation needs for paddy fields in District IV Nagari Bayang Utara until 2025. The MHP electricity production in final year of projection with BAU scenario reaches 0.88 GWh, while with a climate change scenario of 0.63 GWh. The electricity demand fulfilled by MHP is predicted to be 0.39% in the BAU scenario, 0.28% in climate change scenarios, and 0.55% in the electricity savings scenario. Of the three MHP, the MHP Pancuang Taba is the most vulnerable to climate change, while MHP Koto Ranah shows relatively lower fluctuation. The highest staple water requirement is for Pancuang Taba which is 3643.4 thousand m3. The growth of staple water needs until 2025 tends to be constant. and most rice irrigation needs are in agriculture 2 of 976 thousand m3. The growth of irrigation needs of Bayang watershed until 2025 tends to be constant. Most irrigation needs for paddy fields are in irrigation area of “Agriculture 2” reaching 976,000 m3. The growth of irrigation needs in Bayang watershed tends to be constant.

scholarly journals Electricity Forecasting Improvement in a Destination Using Tourism Indicators

2019 ◽  
Vol 11 (13) ◽  
pp. 3656
Author(s):  
Oscar Trull ◽  
Angel Peiró-Signes ◽  
J. Carlos García-Díaz
Keyword(s):  
Power Plants ◽  
Fossil Fuel ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Economic Losses ◽  
Prediction Errors ◽  
Demand Forecast ◽  
Tourist Destination ◽  
Tourist Destinations ◽  
Fossil Fuel Power Plants

The forecast of electricity consumption plays a fundamental role in the environmental impact of a tourist destination. Poor forecasting, under certain circumstances, can lead to huge economic losses and air pollution, as prediction errors usually have a large impact on the utilisation of fossil fuel-generation plants. Due to the seasonality of tourism, consumption in areas where the industry represents a big part of the economic activity follows a different pattern than in areas with a more regular economic distribution. The high economic impact and seasonality of the tourist activity suggests the use of variables specific to it to improve the electricity demand forecast. This article presents a Holt–Winters model with a tourism indicator to improve the effectiveness on the electricity demand forecast in the Balearic Islands (Spain). Results indicate that the presented model improves the accuracy of the prediction by 0.3%. We recommend the use of this type of model and indicator in tourist destinations where tourism accounts for a substantial amount of the Gross Domestic Product (GDP), we can control a significant amount of the flow of tourists and the electrical balance is controlled mainly by fossil fuel power plants.

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