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ElectricalPower Generation
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Which of the following equations is used to estimate the trend term in the deterministic part of load?

A

N ∑ d d σ σ σ = 1 N + ∑ d d σ σ = 1 N ∑ σ σ = 1 N N ∑ σ 2 σ = 1 N + ∑ σ σ = 1 N 2

B

N ∑ d d σ σ σ = 1 N + ∑ d d σ σ = 1 N ∑ σ σ = 1 N N ∑ σ 2 σ = 1 N + ∑ σ σ = 1 N 2

C

N ∑ d d σ σ σ = 1 N - ∑ d d σ σ = 1 N ∑ σ σ = 1 N N ∑ σ 2 σ = 1 N - ∑ σ σ = 1 N 2

D

None of above

Correct Answer

Concept & PrincipleElectricalPower Generation
Option C

N∑ddσσσ=1N-∑ddσσ=1N∑σσ=1NN∑σ2σ=1N-∑σσ=1N2

Quick Summary: The deterministic part of electrical load is often modeled as a linear trend represented by the equation $d_d(\sigma) = a\sigma + b$. The coefficients $a$ and $b$ are determined using the method of least squares regression, where the slope $a$ is calculated to minimize the sum of squared residuals between the observed and predicted values.

💡 Explanation

The deterministic part of electrical load is often modeled as a linear trend represented by the equation dd(σ)=aσ+bd_d(\sigma) = a\sigma + bdd​(σ)=aσ+b. The coefficients aaa and bbb are determined using the method of least squares regression, where the slope aaa is calculated to minimize the sum of squared residuals between the observed and predicted values.

🔢 Key Formulas

dd(σ)=aσ+bd_d(\sigma) = a\sigma + bdd​(σ)=aσ+b — Linear trend equation

a=N∑(dd(σ)σ)−∑dd(σ)∑σN∑σ2−(∑σ)2a = \frac{N\sum(d_d(\sigma)\sigma) - \sum d_d(\sigma)\sum \sigma}{N\sum \sigma^2 - (\sum \sigma)^2}a=N∑σ2−(∑σ)2N∑(dd​(σ)σ)−∑dd​(σ)∑σ​ — Slope of the deterministic trend

⚙️ Working Principle

In linear regression, to minimize S=∑[dd(σ)−(aσ+b)]2S = \sum [d_d(\sigma) - (a\sigma + b)]^2S=∑[dd​(σ)−(aσ+b)]2, we take partial derivatives with respect to aaa and bbb and set them to zero. Solving the resulting normal equations yields the slope a=N∑(dd(σ)σ)−∑dd(σ)∑σN∑σ2−(∑σ)2a = \frac{N\sum(d_d(\sigma)\sigma) - \sum d_d(\sigma)\sum \sigma}{N\sum \sigma^2 - (\sum \sigma)^2}a=N∑σ2−(∑σ)2N∑(dd​(σ)σ)−∑dd​(σ)∑σ​. This provides the best-fit line for the long-term trend in load data.

📌 Key Points
  • ▸

    The deterministic load component represents the predictable long-term growth or seasonal trends.

  • ▸

    The least squares method ensures the trend line has the minimum total error.

  • ▸

    The denominator represents the variance of the independent variable (time/sigma).

✅ Advantages
  • ▸

    Mathematically precise identification of long-term growth.

  • ▸

    Reduces noise from random load fluctuations.

❌ Disadvantages / Limitations
  • ▸

    Sensitive to outliers in historical data.

  • ▸

    Assumes linear growth which may not hold over very long horizons.

🛠️ Applications / Uses
  • ▸

    Long-term power system planning.

  • ▸

    Peak demand estimation for infrastructure investment.

📄 Additional Information
  • ▸

    The variable N denotes the total number of data points.

  • ▸

    The numerator represents the covariance between the load and time, scaled by N.

  • ▸

    Option A and B are incorrect due to incorrect signs in the denominator leading to divergence.

📊 Diagram / Illustration
Least Squares Estimate: Trend Slope (a)N Σ (dᴅ(σ) σ) - Σ dᴅ(σ) Σ σN Σ σ² - [Σ σ]²
✅

C is correct — The provided equation represents the standard slope calculation for the least squares regression method used to estimate the deterministic trend of load data.

Core Concepts Used
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Load Forecasting Linear Regression Deterministic Trend
💡 EXAM TIP

Remember that for any linear trend estimation y=mx+cy=mx+cy=mx+c via least squares, the denominator must involve the variance of x, which always involves a subtraction: N∑x2−(∑x)2N\sum x^2 - (\sum x)^2N∑x2−(∑x)2.

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