load forecasting technique

1. Load Forecasting Techniques
A case study : New England last
3days on 2002
Renewable Energy and Smart
Grid course
MYO ZAW OO(3122999029)

2. 1
Forecasting Horizon’s Duration
*Exponential Smoothing Technique
*Procedures of Simple exponential smoothing method
Comparison of results

3. INTRODUCTION
• load forecasting involves predicting the future demand for electricity or
other resources on the power grid.
• This can help utilities and other energy providers plan for future energy
needs and ensure that they have enough capacity and resources to meet
those needs.
• There are various methods that can be used for load forecasting, such as
time-series analysis, artificial intelligence and machine learning
algorithms, as well as statistical modeling.
• Load forecasting is an important aspect of energy management and
sustainability, and it plays a crucial role in ensuring the stability and
reliability of power systems.

4. Add your title
Add your words here,according
to your need to draw the text
box size
Forecasting Horizon’s Duration

5. Exponential Smoothing Technique
1- The simple exponential smoothing method :
 suitable for forecasting data with no clear trend or seasonal pattern.
 a time series forecasting technique that can forecast data with no trend or seasonal pattern .
 calculating the weighted average of past observations, with the weights decreasing exponentially
as the observations get older.
2- Holt’s method :(double/damped exponential smoothing)
 exponential smoothing method used for time series forecasting that includes a trend component,
unlike simple exponential smoothing.
 Makes use of two different parameters and allows forecasting for series with trend .
3- Holt’s – Winters’ method:(triple exponential smoothing)
 involves three smoothing parameters to smooth the data, the trend , and the seasonal index.

6. Procedures of Simple exponential smoothing method:
Step 1: use the mean of the series as the initial Ft at time period t=0
𝐹1 = 𝑦 =
𝑡=1
𝑛
𝑦𝑡
𝑛
Step 2: Calculate the updated estimate by using formula
𝐹𝑡+1 = 𝛼𝑦𝑡 + (1 − 𝛼)𝐹𝑡
where 𝛼 is smoothing constant between 0 and 1.
Step 3:Calculate Forecast Errors:
mean absolute deviation (MAD) = 𝑡=1
𝑛
𝐹𝑡 − 𝑦𝑡 /𝑛
Mean squared error (MSE) = 𝑡=1
𝑛
(𝐹𝑡−𝑦𝑡)2/𝑛
mean absolute percentage error(MAPE) = 𝑡=1
𝑛
𝐹𝑡 − 𝑦𝑡 /𝑦𝑡 /n

7. Add your title
Add your words here,according
to your need to draw the text
box size
1st day of 2002
When α=0.2
MAD= 0.047834057 & MAPE= 0.098500406
When α=0.8
MAD= 0.024304381 & MAPE= 0.049728335

8. 2nd day of 2002
When α=0.2
MAD= 0.062811087 & MAPE= 0.121936212
When α=0.8
MAD= 0.032535504 & MAPE= 0.062969068

9. Add your title
Add your words here,according
to your need to draw the text
box size
3rd day of 2002
When α=0.2
MAD= 0.057253517 & MAPE= 0.11448434
When α=0.8
MAD= 0.029944145 & MAPE= 0.059418365

10. THANK YOU