Abstract
We propose a new forecasting procedure that includes randomized hierarchical dynamic regression models with random parameters, measurement noises and random input. We developed the technology of entropy-randomized machine learning, which includes the estimation of characteristics of a dynamic regression model and its testing by generating ensembles of predicted trajectories through the sampling of the entropy-optimal probability density functions of the model parameters and measurement noises. The density functions are determined at the learning stage by solving the constrained maximization problem of an information entropy functional subject to the empirical balances with real data. The proposed procedure is applied to the randomized forecasting of the daily electrical load in a regional power system. We construct a two-layer dynamic model of the daily electrical load. One of the layers describes the dependence of electrical load on ambient temperature while the other simulates the stochastic quasi-fluctuating temperature dynamics.
Highlights
Due to the gradually increasing resources and computational power of computers, huge amounts of data can be accumulated and stored, both in natural and digitized formats
Data are a fundamental component of the three concepts above: data are adopted for estimating the characteristics of a model using machine learning (ML) procedures, which allows extracting new knowledge
We propose a fundamentally different forecasting method—the so-called entropy-randomized forecasting (ERF)
Summary
Due to the gradually increasing resources and computational power of computers, huge amounts of data can be accumulated and stored, both in natural and digitized formats. The following question arises immediately: what should be done with these data, except for storage? Extracting new knowledge from data seems to be a very interesting idea. The concepts of Data Mining (DM) [1,2], Big Data (BD) [3] and Data Science (DS) [4] were formulated and further developed by researchers . A very tempting goal—extracting new knowledge from data—inevitably leads to the verbal or formal (mathematical) modeling of the “expected” knowledge. Any model has some predictive properties, which can be implemented only under known values of its quantitative characteristics (parameters). Data are a fundamental component of the three concepts above: data are adopted for estimating the characteristics of a model using machine learning (ML) procedures, which allows extracting new knowledge
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