Forecasting Occupancy for Demand Driven HVAC Operations Using Time Series Analysis

Abstract

Building heating, ventilation, and air conditioning (HVAC) systems contribute substantially to the energy consumption of buildings. Today, traditional HVAC systems mostly operate according to the maximum occupancy assumption, which in turn increases energy consumption during periods of low occupancy. Although, recently, implementing demand-driven HVAC operations are accepted as an innovate approach for reducing HVAC-related energy consumption, occupancy forecast is important to realize demand-driven HVAC operations in buildings. This study aims at using time series models in order to forecast the daily number of bank customers in a financial center of a bank, which is located in Izmir, Turkey. Data were collected from the computerized tracking system for a period of 60 weeks and two forecasting methods were used: 1) Decomposition Method, 2) Box-Jenkins Method. To determine the final model identified via the Box-Jenkins Method, goodness-of-fit, residual analysis and Akaike information criterion were taken into consideration. The results show that the SARIMA model with a MAPE of 11% yields a good occupancy forecast for supporting demand-driven HVAC operations.

Application of time series models for heating degree day forecasting

“…In the analysis of consecutive data, the multiplicative decomposition approach is preferred. Therefore, in this study, this approach was used, in which time series can be expressed as the product of the four components of the series and are Y = T.S.C.I, where Y represents the original data, whereas T, S, C, and I represent the trend, seasonal, cyclical, and random components of the series, respectively (Calis et al 2017). In this method, first, the effect of the random change is reduced by applying an exponential correction to the data.…”

SARIMA Modelling for Forecasting the Electricity Consumption of a Health Care Building

“…Python language is used to identify the SARIMA models. The construction of the models by means of Box-Jenkins procedure [3], [5] has the following steps:…”

Analyzing and modeling thermal complaints in a commercial building in France

“…In addition, Azizpour et al (2013) indicated that the actual thermal dissatisfaction of occupants in a hospital was greater than the predicted dissatisfaction obtained via the PMV-PPD model. Apart from building and climate-related characteristics, some research indicated that occupant-related parameters such as gender and age also have a correlation with thermal complaints (Ceria and De Dear 2001;Choi et al 2010;Calis et al 2018). It can be concluded that predicting thermal complaints is not an easy task.…”