Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast.Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year.

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Demand forecasting is a procedure for identifying the electrical demand that can be expected from a specified number of consumers in a specified period. Electrical demand and electrical supply system could be in terms of average system demand, maximum system demand, and load demand in MW or energy demand in MWhr. Demand forecasting is usually undertaken with the prediction of hourly, daily, weekly and annually of the system demand and peak demand. Categorized form of load forecasting are short-term, longterm and medium-term which takes a few hours ahead of few weeks, one week to one year and five years to twenty years etc. This paper describes the demand forecasting of Kerala power system by using two different methods and a comparative analysis on its impact on the accuracy of load forecasting. This paper also present an error reduction based demand forecasting of Kerala power system. The performance evaluation parameters MAPE, MSE, RMSE, MAE/MAD and percentage error have been used for testing this proposed forecasting models with error reduction strategies.

The economic load dispatch problem aims at controlling the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying the power demand and system equality and inequality constraints. The economic load dispatch is a non-linear constrained optimization method whose complexity increases when constraints such as system power balance constraints and generator constraints are considered. This paper describes the use of particle swarm optimization algorithm in finding out which combination of generators should be worked together in order to meet the required load demand at minimum operating cost