Curve fitting and regression line method based seasonal short term load forecasting

Short term load forecasting in this paper is done by considering the sensitivity of the network load to the temperature, humidity, day type parameters (THD) and previous load and also ensuring that forecasting the load with these parameters can best be done by the Regression Line Method (RLM) and Curve Fitting Method (CFM). The analysis of the load data recognizes that the load pattern is not only dependent on temperature but also dependent on humidity and day type. A new norm has been developed using the regression line concept with inclusion of special constants which hold the effect of the history data and THD parameters on the load forecast and it is used for the STLF of the test dataset of the data set considered. A unique norm with a, b, c and d constants based on the history data has been proposed for the STLF using the concept of curve fitting technique. The algorithms implementing this forecasting technique have been programmed using MATLAB. The input data of each day average power, average temperature, average humidity and day type of the previous year are used for prediction of power, in the case of the regression line method and the forecast previous month data and the similar month data of the previous year is used for the curve fitting method. The results are also compared with the Euclidean Norm Method (ELM) which is generally used method for STLF. The simulation results show the robustness and suitability of the proposed CFM norm for the STLF as the forecasting accuracies are very good and less than 3% for almost all the day types and all the seasons. Results also indicate that the proposed curve fitting method out passes the regression technique and the standard Euclidean distance norm with respect to forecasting accuracy and hence it will provide a better technique to utilities for short term load forecasting.

I. Introduction

Load forecasting is an important component for power system's energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. Besides playing a key role in reducing the generation cost, it is also essential to the reliability of power systems. With the recent trend of deregulation of electricity markets, STLF has gained more importance and greater challenges. Many factors play a vital role in efficient STLF of which most important is weather. Weather is defined as the atmospheric condition existing over a short period in a particular location. It is often difficult to predict and it can vary significantly even over a short period. Climate also varies with time: seasonally, annually and on a decade's basis [1]. The relationship between demand and temperature is non linear with the demand increasing for both low and high temperature [2]. The range of the possible approaches to the forecast is to take a microscopic view of the problem and try to model the future load as a reflection of previous [3]. In the case of large variation in the temperature compared to that of the previous year, the load also changes accordingly. In such cases there would be the shortage of similar days' data and the task of the forecasting load is very difficult [4].