Forecasting Methods in Electric Power Sector

Electricpowerplaysavibrantroleineconomicgrowthanddevelopmentofaregion.Thereisastrong co-relationbetweenthehumandevelopmentindexandpercapitaelectricityconsumption.Providing adequateenergyofdesiredqualityinvariousformsinasustainablemannerandatacompetitiveprice isoneofthebiggestchallenges.Tomeetthefast-growingelectricpowerdemand,onasustained basis,meticulouspowersystemplanningisrequired.Thisplanningneedselectricalloadforecasting asitprovidestheprimaryinputsandenablesfinancialanalysis.Accurateelectricloadforecastsare helpfulinformulatingloadmanagementstrategiesinviewofdifferentemergingeconomicscenarios, whichcanbedovetailedwiththedevelopmentplanoftheregion.Theobjectiveofthisarticleisto understandvariouslongtermelectricalloadforecastingtechniques,toassessitsapplicability;and usefulnessfor longtermelectrical loadforecastingforanisolatedremoteregion,underdifferent growthscenariosconsideringdemandsidemanagement,priceandincomeeffect. KEywORdS Artificial Neural Network, Electrical Energy Consumption, Electrical Energy Requirements, Long Term Electrical Load Forecasting, Parametric

[1]  M. M. Ardehali,et al.  LONG-TERM ELECTRICAL ENERGY CONSUMPTION FORECASTING FOR DEVELOPING AND DEVELOPED ECONOMIES BASED ON DIFFERENT OPTIMIZED MODELS AND HISTORICAL DATA TYPES , 2014 .

[2]  Chusak Limsakul,et al.  The Comparision of Mid Term Load Forecasting between Multi-Regional and Whole Country Area Using Artificial Neural Network , 2010 .

[3]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[4]  D.W. Bunn,et al.  Forecasting loads and prices in competitive power markets , 2000, Proceedings of the IEEE.

[5]  Chusak Limsakul,et al.  A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher , 2010 .

[6]  Pu Wang,et al.  Fuzzy interaction regression for short term load forecasting , 2014, Fuzzy Optim. Decis. Mak..

[7]  Xie Da,et al.  The physical series algorithm of mid-long term load forecasting of power systems , 2000 .

[8]  Amira S. Ashour,et al.  Renewable Energy Management with a Multi-Agent System , 2015, Int. J. Energy Optim. Eng..

[9]  S. Pandian,et al.  Fuzzy approach for short term load forecasting , 2006 .

[10]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

[11]  O. Ebohon,et al.  Energy, economic growth and causality in developing countries: A case study of Tanzania and Nigeria , 1996 .

[12]  Ahmad Rizal Mohd Yusof,et al.  A review of MARKAL energy modeling , 2009 .

[13]  Aie-Rie Lee,et al.  Cointegration, error-correction, and the relationship between GDP and energy:: The case of South Korea and Singapore , 1998 .

[14]  John E. Boylan,et al.  Reproducibility in forecasting research , 2015 .

[15]  Massimo Filippini,et al.  Swiss residential demand for electricity by time of use: An application of the almost ideal demand system , 1995 .

[16]  Mohamed Chaouch,et al.  Clustering-Based Improvement of Nonparametric Functional Time Series Forecasting: Application to Intra-Day Household-Level Load Curves , 2014, IEEE Transactions on Smart Grid.

[17]  N. Hatziargyriou,et al.  An Annual Midterm Energy Forecasting Model Using Fuzzy Logic , 2009, IEEE Transactions on Power Systems.

[18]  Jaime Lloret,et al.  Artificial neural networks for short-term load forecasting in microgrids environment , 2014 .

[19]  S. M. El-Debeiky,et al.  Long-Term Load Forecasting for Fast-Developing Utility Using a Knowledge-Based Expert System , 2002, IEEE Power Engineering Review.

[20]  Tao Hong,et al.  Long Term Probabilistic Load Forecasting and Normalization With Hourly Information , 2014, IEEE Transactions on Smart Grid.

[21]  Haidar Samet,et al.  A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting , 2014, Expert Syst. Appl..

[22]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Theodoros Zachariadis,et al.  Forecast of Electricity Consumption in Cyprus up to the Year 2030: The Potential Impact of Climate Change , 2010 .

[24]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[25]  B. Kermanshahi,et al.  Up to year 2020 load forecasting using neural nets , 2002 .

[26]  Shangyou Hao,et al.  An implementation of a neural network based load forecasting model for the EMS , 1994 .

[27]  José Manuel Andújar Márquez,et al.  An Energy Management Strategy and Fuel Cell Configuration Proposal for a Hybrid Renewable System with Hydrogen Backup , 2017, Int. J. Energy Optim. Eng..

[28]  D. Kamerschen,et al.  The demand for residential, industrial and total electricity, 1973-1998 , 2004 .

[29]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.