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The correlation technique relates system load to
Various demographic factors
Economic factors
Both a and b
None of the above
Both a and b
Quick Summary: The correlation technique for load forecasting determines the future electrical system load by identifying statistical relationships between the load and exogenous variables such as demographics and economic growth. This method assumes that power consumption is a dependent variable influenced by independent factors like population size, per capita income, and industrial production indices.
The correlation technique for load forecasting determines the future electrical system load by identifying statistical relationships between the load and exogenous variables such as demographics and economic growth. This method assumes that power consumption is a dependent variable influenced by independent factors like population size, per capita income, and industrial production indices.
L=ฮฒ0โ+ฮฒ1โX1โ+ฮฒ2โX2โ+ฯต โ Multi-variable linear regression model where L is load, Xnโ are socio-economic factors, and ฮฒ are coefficients.
The principle relies on regression analysis, where the system load L is expressed as a function of various parameters: L=f(x1โ,x2โ,...,xnโ). By processing historical data for both the load and the influential factors, the utility derives correlation coefficients to quantify the sensitivity of power demand to changes in socio-economic or demographic environments. It is effectively a top-down approach used for long-term planning.
Correlation technique is a primary tool for long-term load forecasting.
Economic variables (GDP, Industrial index) correlate strongly with peak demand.
Demographic shifts (urbanization, population growth) directly impact residential load patterns.
Accuracy depends on the quality of historical data for all correlated variables.
Useful for long-range planning (5-20 years).
Provides insight into how growth in different sectors impacts the grid.
Data availability and reliability for auxiliary variables can be poor.
Cannot capture sudden, localized, or transient changes effectively.
Utility long-term generation capacity planning.
Regional grid transmission infrastructure expansion.
This technique treats load as a dependent variable in a multivariate statistical framework.
Option A and B are both standard categories of independent variables used in this model, making C the comprehensive choice.
C is correct โ The correlation technique integrates both demographic shifts and economic indicators to model and project future power system demand.
In exams, remember that 'Correlation' techniques focus on external macro-drivers, whereas 'Time-series' techniques (like moving averages) focus strictly on historical load data patterns.