Forecasting summer-time overheating in UK homes using time series models

Heatwaves are projected to become more frequent, intense and long-lasting in the UK and the prevalence of overheating in dwellings is set to increase. As a result, occupants will experience increased levels of thermal discomfort, heat stress and heat-related morbidity and mortality. Since the use of mechanical air conditioning in dwellings is unsustainable, and not widely affordable, it is of utmost importance to understand when heat related health risks are anticipated in free-running dwellings. This is crucial for vulnerable occupants, such as the elderly, for whom the accurate detection of future heat risks could prepare them (or their carers) for timely mitigation, for example, through additional window ventilation or the use of shading. Many countries deploy Heat-Health Warning Systems (HHWS) to alert their populations, however, these generally apply to a wide area and are based exclusively on regional weather forecasts. Consequently, HHWSs are unable to identify where, when, or to what extent individual buildings (and their occupants) will be affected. Previous studies have investigated the use of time series forecasting models, with the majority considering the use of Model Predictive Control. There is, however, no rigorous scientific evidence to support the belief that such models can provide accurate predictions in free-running dwellings during heatwaves and over multi-day forecasting horizons. This thesis therefore examines the use of black-box forecasting models to provide reliable predictions of the impending indoor temperatures in UK homes. Having established the viability of this approach, the application of such models in the context of an indoor Heat-Health Warning System (iHHWS) has been explored. This research led to five main findings: (i) linear AutoRegressive forecasting models with eXogenous inputs (ARX), i.e. weather forecasts, can provide satisfactory accuracies during heatwaves for time horizons up to 72 h ahead; (ii) more complex semi-parametric Generalized Additive Models (GAMs) were not capable of significantly improving the forecasting accuracy at forecasting horizons over 6 h (iii) logistic GAMs can predict the window opening state with adequate discrimination, however, integration of the window state into forecasting models did not improve their accuracy; (iv) forecasting models could be usefully incorporated within an iHHWS, however, the warning lead-time should be constrained to less than 24 h in order to guarantee high confidence in such a system; (v) a weighted metric such as the Cumulative Heat Index (CHI) could further reduce the risks of false or missed warnings, increasing the dependability of the iHHWS.</div

Can semi-parametric additive models outperform linear models, when forecasting indoor temperatures in free-running buildings?

A novel application combining semi-parametric Generalized Additive Models (GAMs) with logistic GAMs was developed to forecast indoor temperatures and window opening states during prolonged heatwaves. GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. Comparison of the models showed that whilst GAMs are capable of improving the forecasting accuracy, the improvements are significant only up to 3-6 hours ahead. During heatwaves and over longer forecasting horizons, GAMs were found to be less reliable and accurate than ARX models. The marginal improvement in forecasting accuracy at shorter horizons did not justify the additional computational time and risk of instability associated with more complex GAMs, at longer forecasting horizons. Whilst, logistic GAMs were shown to adequately predict the window opening state, incorporating knowledge of the window state did not significantly improve the accuracy of the indoor temperature predictions

Forecasting indoor temperatures during heatwaves using time series models

Early prediction of impending high temperatures in buildings could play a vital role in reducing heat-related morbidity and mortality. A recursive, AutoRegressive time series model using eXogenous inputs (ARX) and a rolling forecasting origin has been developed to provide reliable short-term forecasts of the internal temperatures in dwellings during hot summer conditions, especially heatwaves. The model was tested using monitored data from three case study dwellings recorded during the 2015 heatwave. The predictor variables were selected by minimising the Akaike Information Criterion (AIC), in order to automatically identify a near-optimal model. The model proved capable of performing multi-step-ahead predictions during extreme heat events with an acceptable accuracy for periods up to 72 h, with hourly results achieving a Mean Absolute Error (MAE) below 0.7 °C for every forecast. Comparison between ARX and AutoRegressive Moving Average models with eXogenous inputs (ARMAX) models showed that the ARX models provided consistently more reliable multi-step-ahead predictions. Prediction intervals, at the 95% probability level, were adopted to define a credible interval for the forecast temperatures at different prediction horizons. The results point to the potential for using time series forecasting as part of an overheating early-warning system in buildings housing vulnerable occupants or contents

Autoregressive neural networks with exogenous variables for indoor temperature prediction in buildings

Thermal models of buildings are helpful to forecast their energy use and to enhance the control of their mechanical systems. However, these models are building-specific and require a tedious, error-prone and time-consuming development effort relying on skilled building energy modelers. Compared to white-box and gray-box models, data-driven (black-box) models require less development time and a minimal amount of information about the building characteristics. In this paper, autoregressive neural network models are compared to gray-box and black-box linear models to simulate indoor temperatures. These models are trained, validated and compared to actual experimental data obtained for an existing commercial building in Montreal (QC, Canada) equipped with roof top units for air conditioning. Results show that neural networks mimic more accurately the thermal behavior of the building when limited information is available, compared to gray-box and black-box linear models. The gray-box model does not perform adequately due to its under-parameterized nature, while the linear models cannot capture non-linear phenomena such as radiative heat transfer and occupancy. Therefore, the neural network models outperform the alternative models in the presented application, reaching a coefficient of determination R2 up to 0.824 and a root mean square error down to 1.11 °C, including the error propagation over time for a 1-week period with a 5-minute time-step. When considering a 50-hour time horizon, the best neural networks reach a much lower root mean square error of around 0.6 °C, which is suitable for applications such as model predictive control

Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1 degrees C and 2 degrees C, respectively

Prediction of internal temperatures during hot summer conditions with time series forecasting models

A novel application using adaptive autoregressive time series forecasting with exogenous inputs (i.e. ARX) has been developed in order to provide reliable short-term forecasts of the internal temperatures in dwellings during hot summer conditions (i.e. heatwaves). The study shows that with proper selection of the predictors, based on the Akaike Information Criterion (AIC), the forecasts provide acceptable accuracy for periods up to 72 hours. The hourly results for the analysed dwellings showed a Mean Absolute Error (MAE) below 0.63°C and 0.49°C for the two case study dwellings across the 3-day forecasting period, during the 2015 heatwave. These findings point to the potential for using time series forecasting as part of an overheating warning system in buildings, especially those housing vulnerable occupants