2 Electricity Demand Analysis and Forecasting - the Tradition Is Questioned !

نویسندگان

  • N. Vijayamohanan Pillai
  • Vijayamohanan Pillai
  • Indrani Chakraborty
چکیده

The present paper seeks to cast scepticism on the validity and value of the results of all earlier studies in India on energy demand analysis and forecasting based on time series regression, on three grounds. (i) As these studies did not care for model adequacy diagnostic checking, indispensably required to verify the empirical validity of the residual whiteness assumptions underlying the very model, their results might be misleading. This criticism in fact applies to all regression analysis in general. (ii) As the time series regression approach of these studies did not account for possible non-stationarity (i.e., unit root integratedness) in the series, their significant results might be just the misleading result of spurious regression. They also failed to benefit from an analytical framework for a meaningful long-run equilibrium and short-run ‘causality’ in a cointegrating space of error correction. (iii) These studies, by adopting a methodology suitable to a developed power system in advanced economies, sought to correlate the less correlatables in the context of an underdeveloped power system in a less developed economy. All explanations of association of electricity consumption in a hopeless situation of chronic shortage and unreliability with its generally accepted ‘causatives’ (as in the developed systems) of population, per capita income, average revenue, etc., all in their aggregate time series, might not hold much water here. Our empirical results prove our secepticism at least in the context of Kerala power system. We find that the cost of dispensing with model adequacy diagnosis before accepting and interpreting the seemingly significant results is very high. We find that all the variables generally recognised for electricity demand analysis are non-stationary, I(1). We find that all the possible combinations of these I(1) variables fail to be explained in a cointegrating space and even their stationary growth rates remain unrelated in the Granger-‘causality’ sense. JEL Classification: C22, C32, C53, L94, Q41.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Iran's Electrical Energy Demand Forecasting Using Meta-Heuristic Algorithms

This study aims to forecast Iran's electricity demand by using meta-heuristic algorithms, and based on economic and social indexes. To approach the goal, two strategies are considered. In the first strategy, genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA) are used to determine equations of electricity demand based on economic and social ind...

متن کامل

Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

متن کامل

Comparative Analysis of Short-Term Price Forecasting Models: Iran Electricity Market

As the electricity industry has changed and became more competitive, the electricity price forecasting has become more important. Investors need to estimate future prices in order to take proper strategy to maintain their market share and to maximize their profits. In the economic paradigm, this goal is pursued using econometric models. The validity of these models is judged by their forecastin...

متن کامل

Short Term Load Forecasting by Using ESN Neural Network Hamedan Province Case Study

Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...

متن کامل

پیش بینی تقاضای بلندمدت انرژی الکتریکی با استفاده از الگوریتم ترکیبیِ عصبی- فازی و انبوه ذرات

  Storing the electrical energy in large scale is impossible. So, it is necessary to identify the factors affecting the electricity demand. Researchers have used different methods to forecast the future demand of electricity, among them intelligent methods and fuzzy based methods are more popular. Since ANFIS structure is based on researcher’s experience about phenomenon, the created structure ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2001