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 Types of load forecasting in power system

(Short-term forecasts(one hour to a week
Medium forecasts(a month up to a year)
Long term forecasts(over one year)

Requirements

Short - term forecast

Engage enough capacity to meet anticipated demand and maintain the spinning reserve needed

Medium forecasts

 

Suitable for planning power outages and maintenance, as well as load switching process

Long term forecasts

 

Required to plan future system capacity requirements and prepare maintenance schedules for generation units

v Accurate Forecasting Factor of loads in power systems

Ø Weather influence
Ø Time factors
Ø Customer classes

1)    Weather Influence

·       There is a clear connection between electric charge and temperature. The most important variables which are responsible for changes in load are.
·       Temperature on dry and wet bulbs.
·       The Point of Dew
·       Humidity
·       Wind direction / Wind speed
·       Cover to the sky
·       Sunshine

2)    Time factors

We should also consider time factors in the forecasting model, like
·       The day of the week
·       The hour of the day
·       Holidays

3)    Customer classes

·       Customer load requests can be broadly divided into three groups:
·       Industrial loads.
·       Residential loads.
·       Commercial loads.
·       Residential loads have the highest annual growth rate and most seasonal fluctuations.
·       This is due to the widespread use of weather-sensitive devices such as space heaters and air conditioners.
·       Commercial loads are also marked by seasonal fluctuations.
·       Industrial loads are basic containing
·       Little weather-based contrast

Short-term forecast In power system

Short-term forecasting usually takes place 24 hours ahead of when the weather forecast for the next day is available. It is composed of four parts

1.    Base Load )L_p)

The base load is the result of the service area's business and economic conditions, and is the largest component of total system load

2.    Weather - Dependent Load (L_w)

Weather contributes significantly to the dynamics of a load and a great deal of effort has been made to find a viable relationship between the environment and the load so as to establish an accurate load model .

The common weather variables are dry-bulb temperature, wind speed, humidity, and daylight illumination, which are usually used to model weather dependent load. Typically the last of these weather variables is the least important, and as its metering is difficult and expensive it is generally excluded from most models. First, the general effects of these environmental variables on load are summarized.

) Temperature  , Wind Speed , Humidity , Wind Speed)

3.   Special events load (L_C)

Therefore, on the system the total load demand "peak value" D is.            D = LB + LW + LC + LR

v Long-Term Load Forecasting

Long-term load forecasting (LTLF): Applicable for long-term planning of networks and systems. There are generally two methods available for that purpose.

    I.          Peak Load Strategy
In this case, the easiest method is to find the trend curve, obtained by  plotting past annual peak values against years of activity
              II.          An approach to energy

·       Another approach is to forecast annual energy sales to various customer groups, such as residential, business, industrial and so on, which can then be translated to annual peak demand using the annual load factor.

·       This approach includes a thorough estimate of factors such as the rate of house building, the selling of electrical appliances, the rise in industrial and commercial activities

 

is the load demand added due to special events or religious and social occasions.

Methods of the Long-Term Electric Load Forecasting

v Parametric Methods
The parametric methods are based on a mathematical model linking load demand to its influencing factors. The parameters of the model are calculated using statistical techniques on historical load data and it affects factors
v Artificial Intelligence based Methods
Artificial methods based on intelligence can solve nonlinear problems and can be useful for long-term load forecasting due to nonlinear load behavior.

                                          Parametric Methods

Trend Analysis

Trend analysis extends beyond electricity demand levels into the future, using techniques ranging from manually drawn straight lines to complex curves generated by computers. Trend forecasting focuses on historical increases in demand for electricity and uses them to forecast potential changes in demand for electricity.

End - Use Modeling

The end-use approach measures energy use directly by using detailed end-user details, such as products, consumer use, age, housing sizes, and so on. End-use models thus explain the energy demand as a function of the number of market applications.

Econometric modeling

The benefit of econometrics is that it offers comprehensive details about future rates of demand for electricity, why future demand for electricity is that and how the demand for electricity is influenced by all the various factors

References

[1]. H.K. Temraz, V.H. Quintana, “Analytic spatial electric load forecasting methods”: a survey,Can. J. Elect. Comp. Eng. 17 (1) (1992)

[2]. I. Drezga, S. Rahman, “Short-term load forecasting with local ANN predictors”, IEEE Trans.Power Syst. 14 (3) (1999)

[3]. P.A. da Silva, L.S. Moulin, “Confidence intervals for neural network based short-term load forecasting”, IEEE Trans. Power Syst. 15 (4) (2000)

[4]. D. Srinivasan, M.A. Lee, “Survey of hybrid fuzzy neural approach to electric load forecasting”, IEEE Int. Conf. Syst. Man Cybern. 5 (1995)

[5]. V.M. Vlahovic, I.M. Vujosevic, “Long-term forecasting: a critical review of direct-trend extrapolation methods”, Int. J. Electr. Power Energ. Syst. 9 (1) (1987)

[6]. E.H. Barakat, S.A. Al-Rashid, “Long-term peak demand forecasting under conditions of high growth”, IEEE Trans. Power Syst. 7 (4) (1992)

[7]. L. Chenhui, “Theory and Methods of Load Forecasting of Power Systems”, Haerbin Institute of Technology Press, China, 1987