Data-driven short-term load forecasting for heating and cooling demand in office buildings

Short-term forecasts of energy demand in buildings serve as key information for various operational schemes such as predictive control and demand response programs. Despite this, developing forecast models for heating and cooling loads has received little attention in the literature compared to models for electricity load. In this paper, we present data-driven approaches to forecast hourly heating and cooling energy use in office buildings based on temporal, autoregressive, and exogenous variables. The proposed models calculate hourly loads for a horizon between one hour and 12 hours ahead. Individual models based on artificial neural networks (ANN) and change-point models (CPM) as well as a hybrid of the two methods are developed. A case study is conducted based on hourly thermal load data collected from several office buildings located on the same campus in Ottawa, Canada. The models are trained with more than two years of hourly energy-use data and tested on a separate part of the dataset to enable unbiased validation. The results show that the ANN model can achieve higher forecasting accuracy for the longest forecast horizon and outperforms the results obtained by a Naïve approach and the CPM. However, the performance of the hybrid CPM-ANN method is superior compared to individual models for all studied buildings

Detection and interpretation of anomalies in building energy use through inverse modeling

This article presents a study in which three inverse modeling techniques were applied to hourly heating and cooling load data extracted from 35 office buildings in Ottawa, Canada. These modeling techniques were three-parameter change point models, regression trees, and artificial neural networks. The change point models were trained with outdoor temperature data, whereas the other two models were trained with four regressors: outdoor temperature, wind speed, horizontal solar irradiance, and a binary work hours indicator. The correlations among the change point model parameters of individual buildings were analyzed. The sensitivity of heating and cooling load intensities to the four regressors was examined. The models were used to identify several types of energy use anomalies. The anomalies detected by different modeling techniques were generally in agreement. The results indicate that nearly half of the buildings did not have effective after-hours schedules to save energy. In all but three buildings, the cooling change point temperature was lower than the heating change point temperature—indicating a simultaneous heating and cooling problem. Moreover, a few buildings with anomalies potentially related to high air infiltration or overventilation, high thermal conductance, and high solar heat gains during summer were identified

Development of a clustering-based morning start time estimation algorithm for space heating and cooling

The morning start time of heating and cooling equipment plays an important role in the energy and comfort performance of buildings. Existing algorithms to guide this decision require either many data types with a consistent labelling nomenclature or a detailed calibrated model. In this paper, a model-free clustering-based morning start time estimation algorithm is put forward. The algorithm inputs only four types of data: indoor and outdoor temperatures, and heating and cooling energy use, and does not require any information regarding the location of the temperature sensors. The algorithm consists of four steps. The first one employs clustering to form groups of zones with a similar temperature response. The second one searches for inflection points to identify cluster temperature change rates during morning start-up periods. The third one determines the start time based on previous morning start-up temperature change rates. The last one estimates the energy savings potential by using bivariate change point models. The algorithm was developed by using a dataset from a large office building. Through hierarchical clustering, the data from 142 temperature sensors were consolidated to only seven clusters. The median morning start-up temperature change rates in individual clusters were between 0.3°C/h and 0.8°C/h for heating, and between -0.5°C/h and -1.2°C/h for cooling. The savings potential by tuning daily start times based on this information was estimated as 3% and 7% for heating and cooling, respectively

Modelling and analysis of unsolicited temperature setpoint change requests in office buildings

This paper presents an analysis conducted upon custom temperature setpoint change request data collected from four large office buildings. In three of these buildings, the setpoint changes were executed upon hot and cold complaints; and in the fourth building, the occupants could adjust their setpoints through their thermostats. Using concurrent indoor and outdoor temperature records, multivariate logistic regression models predicting the frequency of custom setpoint change requests were developed. The indoor temperatures that minimize the frequency of thermal complaints or thermostat overrides were determined with these models. It is argued that these temperatures can be used to select building specific default seasonal temperature setpoints. The results indicate that the majority of the setpoint change requests were either to increase the default 22 °C temperature setpoints during the cooling season or to decrease them during the heating season. Custom setpoint change requests were registered 24 times more frequently when occupants were permitted to make temporary thermostat overrides. It was also identified that the operators tend to make greater setpoint changes upon hot and cold complaints than the occupants make through their thermostat overrides

Model-based and data-driven anomaly detection for heating and cooling demands in office buildings

A considerable portion of total energy loss within the built environment originates from operational errors during the actual lifespan of a building. With the rise of fully automated commercial buildings, a large amount of sensory data is becoming available that can be leveraged to detect and predict such errors. However, processing these data on-site requires significant knowledge and effort by building op this work, a combination of model-based and data-driven approaches are employed to facilitate the analysis of historical energy demand data. Using change-point models and symbolic quantisation techniques, a large dataset of heating and cooling demand profiles several office buildings are transformed into a format that is easily interpreted by the building operator and is suitable for actionable anomaly detection. Further quantification of anomalies and calculation of potential savings are drawn from the results

Opportunistic occupancy-count estimation using sensor fusion: A case study

Estimation of occupancy counts in commercial and institutional buildings enables enhanced energy-use management and workspace allocation. This paper presents the analysis of cost-effective, opportunistic data streams from an academic office building to develop occupancy-count estimations for HVAC control purposes. Implicit occupancy sensing via sensor fusion is conducted using available data from Wi-Fi access points, CO 2 sensors, PIR motion detectors, and plug and light electricity load meters, with over 200 h of concurrent ground truth occupancy counts. Multiple linear regression and artificial neural network model formalisms are employed to blend these individual data streams in an exhaustive number of combinations. The findings suggest that multiple linear regression models are the superior model formalism when model transferability between floors is of high value in the case study building. Wi-Fi enabled device counts are shown to have high utility for occupancy-count estimations with a mean R 2 of 80.1–83.0% compared to ground truth counts during occupied hours. Aggregated electrical load data are shown to be of higher utility than separately submetered plug and lighting load data