  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.

Neural and Evolutionary Computing;Computational Engineering, Finance, and Science