Forecasting Time Series for Power Consumption Data in Different Buildings Using the Fractional Brownian Motion

TL;DR

This paper applies fractional Brownian motion theory to forecast short-term power consumption in various buildings, demonstrating its effectiveness on real data from 20 objects across different sectors.

Contribution

It introduces a novel application of fractional Brownian motion for short-term power consumption forecasting across diverse building types.

Findings

Successful power consumption estimation for all studied buildings

Identification of buildings with significant increases in power use

Feasibility of short-term forecasting demonstrated

Abstract

In the present paper will be discussed the problem related to the individual household electric power consumption of objects in different areas-industry, farmers, banks, hospitals, theaters, hostels, supermarkets, universities. The main goal of the directed research is to estimate the active P and full S power consumptions for all studied buildings. The defined goal is achieved by solving of the following three problems. The first problem studies which buildings increase their power consumption. The second one finds which objects have the greatest increase of power consumption. And the third problem regards if it is possible to make a short-term forecast, based on the solutions of previous two problems. The present research and solving of the aforementioned problems is conducted using fractional Brownian motion theory. The applicability of this approach is illustrated on the example with 20 real objects in different areas. The paper ends with conclusion notes about possibilities to make short-term forecasts about power consumption of the considered buildings.