The Forecastability of Underlying Building Electricity Demand from Time Series Data

TL;DR

This paper introduces a data-driven method to assess the inherent forecastability of building electricity demand using historical time series data, aiding in selecting suitable forecasting models for energy management.

Contribution

It presents a novel approach to evaluate the forecastability of building energy demand directly from historical data without relying on specific forecasting models.

Findings

The method effectively quantifies forecastability levels.

It helps identify the most suitable forecasting models for different buildings.

The approach improves energy management decision-making.

Abstract

Forecasting building energy consumption has become a promising solution in Building Energy Management Systems for energy saving and optimization. Furthermore, it can play an important role in the efficient management of the operation of a smart grid. Different data-driven approaches to forecast the future energy demand of buildings at different scale, and over various time horizons, can be found in the scientific literature, including extensive Machine Learning and Deep Learning approaches. However, the identification of the most accurate forecaster model which can be utilized to predict the energy demand of such a building is still challenging.In this paper, the design and implementation of a data-driven approach to predict how forecastable the future energy demand of a building is, without first utilizing a data-driven forecasting model, is presented. The investigation utilizes a historical electricity consumption time series data set with a half-hour interval that has been collected from a group of residential buildings located in the City of London, United Kingdom