Short Term Load Forecasting Using Arima

Short Term Load Forecasting Using Arima

1. INTRODUCTION:

1.1 ABSTRACT:

Short-term load forecasting plays an important role in electric power system operation and planning. An accurate load forecasting not only reduces the generation cost in a power system, but also provides a good principle of effective operation. Load demand forecasting has had important role regarding investments in energy distribution, planning and management strategies. Furthermore, inaccurate forecasting can increase the operational costs.

1.2 SCOPE:

There are several researches regarding short term forecasting but it is particularly more essential to estimate the load demand of the next minutes, to avoid undesirable disturbances, and to perform an accurate load frequency control of energy management systems. Our Project intends to forecast load based on the behavior of previous measures.

1.3 OBJECTIVES:

The primary aim of this project is to design an Arima Model for short term load forecasting. The objectives of the project are as follows:

• To develop an Arima model for load forecasting in order to minimize wastage of resources.

• To minimize costs of energy generation as well as improve the electric power system safety by load forecasting.
• To develop an Arima model which considers the dynamic process of data series, time delay variables and the auto correlation of residuals in order to achieve precise load forecasting.

1.4 METHODOLOGY:

• Autoregressive Integrated Moving Average Model

This model aims to  accurately represents the past and future patterns of the time series, which means, the methodology applied for the ARIMA model estimation aims to find the proper parameters that describe the undermentioned structure:

ARIMA (p, d, q) (P, D, Q)

           Where p is the order of the autoregressive model; d is the number of differentiations, to      accomplish stationarity; q is the order of moving average model; P is the order of seasonal autoregressive model; D is the seasonal differentiations, to accomplish stationarity too; and Q is the order of seasonal moving average model