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The Load Serving Entity (LSE) requires, for its power procurement portfolio management, accurate peak load forecast in medium term (upto six months ahead). A complete description of the random variable, i.e., load, is provided by probability density function. Hence, we consider the problem of forecasting probability density function of hourly peak load during a month. First, we propose a non-parametric model based on the Alternating Conditional Expectation (ACE) to obtain point forecast. Then, by considering multiple scenarios of the weather variables i.e., temperature-humidity tuples, we obtain probability density forecast of the peak load. Out-of-sample testing is used to demonstrate efficacy of the proposed approach.

In this paper, a certain bivariate exponential distribution is used for the spatial prediction. The unobserved random variable is predicted by the projection onto the space of all linear combinations of the powers, up to degree m, of the observed random variables plus the constant 1. We obtain a solution by assuming that all the bivariate distributions follow Gumbel's type III or logistic form of bivariate exponential. The method is implemented on two data sets and the results are presented. The predictions are compared with the original values through Mean Structural Similarity (MSSIM) index of Wang et al. (IEEE Trans Image Process 13(4): [600][601][602][603][604][605][606][607][608][609][610][611][612] 2004). Using the MSSIM index the proposed method is also compared with Ordinary Kriging and with Simple Kriging after normal score transform.