# Short Term Load Forecasting Using Multi Parameter Regression

## TL;DR

This paper presents a multiparameter regression approach for short-term load forecasting using various weather and load parameters, demonstrating acceptable accuracy and potential for future adaptive methods.

## Contribution

It introduces a multiparameter regression model for load forecasting that incorporates multiple weather and load parameters, with implementation in MATLAB.

## Key findings

- Error within tolerable range for forecasts
- Model implemented successfully in MATLAB
- Potential for adaptive regression methods in future

## Abstract

Short Term Load forecasting in this paper uses input data dependent on parameters such as load for current hour and previous two hours, temperature for current hour and previous two hours, wind for current hour and previous two hours, cloud for current hour and previous two hours. Forecasting will be of load demand for coming hour based on input parameters at that hour. In this paper we are using multiparameter regression method for forecasting which has error within tolerable range. Algorithms implementing these forecasting techniques have been programmed using MATLAB and applied to the case study. Other methodologies in this area are ANN, Fuzzy and Evolutionary Algorithms for which investigations are under process. Adaptive multiparameter regression for load forecasting, in near future will be possible.

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Source: https://tomesphere.com/paper/0912.1015