Model Predictive Control In Buildings Research Articles

Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage

Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve model data efficiency is quantified, specifically for: reuse of prediction models, reduction of training data duration, reduction of model data features, and online model training. A simple linear multi-layer perceptron model is shown to provide equivalent forecast accuracy to state-of-the-art models, with greater data efficiency and generalisability. The use of more than 2 years of training data for load prediction models provided no significant improvement in forecast accuracy. Forecast accuracy and data efficiency were improved simultaneously by using change-point analysis to screen training data. Reused models and those trained with 3 months of data had on average 10% higher error than baseline, indicating that deploying MPC systems without prior data collection may be economic.

Economic Model Predictive Control in Buildings Based on Piecewise Linear Approximation of Predicted Mean Vote Index

Energy shortage is a challenge for many countries, and building energy consumption accounts for a considerable proportion of global energy consumption. The main work of this paper is to optimize the energy consumption of heating, ventilating, and air conditioning (HVAC) systems in buildings based on economic model predictive control (EMPC). The cost in EMPC design includes energy consumption and predicted mean vote (PMV), which is an index that evaluates the thermal comfort of indoor occupants. In order to model the nonlinearity of the PMV index, we propose a lattice piecewise linear (PWL) approximation, which has high approximation precision and facilitates the resulting optimization problem, which is basically a piecewise quadratic programming problem. For the piecewise quadratic programming, we propose a descent algorithm that converges quickly and scales well with the length of the prediction horizon in the EMPC problem. The experimental results demonstrate that the proposed method saves 19.78% of the electricity cost compared to the conventional control strategy and significantly increases indoor comfort. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> — The motivation of this article is to provide a control strategy to reduce building energy consumption and ensure indoor thermal comfort. In most of the existing methods for air conditioning temperature control, the occupants’ comfort hasn’t been considered. In this paper, thermal comfort is described by the PMV index, which is basically nonlinear. In order to model the thermal comfort more accurately, in this paper, the PMV index is approximated piecewise linearly in order to meet the requirements of accuracy and computational efficiency. The resulting optimization problem is not hard to solve, and we provide an efficient algorithm for solving this optimization problem. Preliminary simulation experiments demonstrate that this approach is practical, i.e., it achieves energy reduction and ensures thermal comfort. Our strategy, however, has not yet been deployed in real buildings. In future research, we will propose similar techniques for large-scale systems in order to solve energy optimization problems containing multiple thermal zones and realize the proposed technique in real buildings.

Model Predictive Control in buildings with thermal and visual comfort constraints

Model Predictive Control can cope with conflicting control objectives in building energy managements. In terms of user satisfaction, visual comfort has been proven in several studies to be a crucial factor, however thermal comfort is typically considered the only important aspect. Besides human well-being, visual comfort strongly impacts the productivity of the occupants in offices. Therefore, from an economic point of view, it is essential to include visual comfort in Model Predictive Control for buildings. In this paper semi-linear support vector machine is applied to learn suitable models for visual comfort measured by Daylight Glare Probability. The resulting model is incorporated into a Model Predictive Control framework, together with an autoregressive exogenous model accounting for the thermal dynamics of the building. The approach is validated through an extensive numerical case study, and the benefits of including visual comfort and blind control in the Model Predictive Control problem are evaluated. We observe that the proposed Model Predictive Control scheme ensures both the thermal and visual comfort constraints at the expense of 2.2% to 7.2% higher energy consumption compared to the benchmark Model Predictive Control configuration, which considers only the thermal comfort constraints.

Linking dataset quality and MPC in buildings: impact of temporal resolution

This study aims at assessing the impact of dataset quality on the performance of Model Predictive Control (MPC). The dataset feature, which is the target of this study, is temporal resolution, which applies to both data logging and the controller time step. A high temporal resolution might result in a more accurate predictive model, but it increases the need for data storage as well as the computational load on the model training. From the controller side, increasing the temporal resolution might lead to better control performance, but it sabotages the real-time response of the system. First, predictive models are developed based on datasets with different temporal resolutions. Subsequently, these predictive models are implemented within an MPC. Results reveal that decreasing the time step lower than 1 hour does not significantly improve the performance of the MPC. However, increasing the time step of MPC above 1 hour deteriorates its performance. Real-time response of the controller is a crucial criterion which deteriorates as the time step shortens. Hence, a suitable choice of temporal resolution is essential for developing a predictive model and MPC. In our case, a resolution of 1 hour is enough to guarantee a good performance of the controller.

Dynamic horizon selection methodology for model predictive control in buildings

The interest in model predictive control (MPC) for buildings has grown in recent years due to the widespread implementation of dynamic electricity tariffs, energy flexibility and distributed energy resources. The MPC applied on buildings is a computational-based methodology used to optimize the performance of heating, ventilation and air conditioning systems (HVAC) by predicting the energy behavior and minimizing a specific cost function in a determined forecasting horizon. The forecasting horizon is one of the critical parameters in MPC design applied in buildings; it should be long enough to activate the buildings’ flexibility potential, but the computational resources grow exponentially with the horizon increase, which could difficult the real-time operation. Furthermore, long periods of non-occupancy, holidays or abrupt comfort-bound changes can significantly affect the optimal forecasting time horizon length. Unfortunately, very few studies have focused on ascertaining this key optimization process aspect. The contribution of this research paper is to demonstrate, through an innovative methodology, that the optimal horizon length can be dynamically updated according to the effects of building inertia. This methodology is validated by assessing the reduction of the economic costs of a space heating system based on a synthetic representation of an experimental building placed in Germany.

Impact of occupant related data on identification and model predictive control for buildings

Model predictive control (MPC) has shown potential in improving building performance but is bottlenecked by the difficulty in constructing control-oriented models. The challenge lies in evaluating the sufficiency of the model and the data usage beforehand. This paper bridges the knowledge gaps in the interactions between data requirements, model quality, and control performance by integrating real-world measurements and simulation-based experiments. The data usage related to occupancy and Internal heat gain (IHG) was studied considering its importance and the absence of consensus in the literature. Varying occupant-related data sources were tested as RC model inputs, including none, schedule, electricity consumption, CO2 ppm, and ideal measurement. Combinations of model inputs and complexities were examined for prediction and control in an office, a classroom, and multi-zone offices on one floor. The results indicated that the usefulness of data is jointly affected by three factors: measurement suitability, model complexity, and modeling purpose. Given the adequate model structure, satisfying prediction and control performance was achieved in offices with no detailed measurement. Meanwhile, electricity and CO2 were needed together to capture the IHG influence and realize the good performance for classrooms. The experiments also uncovered the heterogeneous requirements on models from traditional prediction tests and the control tasks. Lower prediction error did not always mean better control. More importantly, we provided the first quantitative demonstration of the complementary relationship between model adequacy and data informativeness with respect to different purposes. This study advocates the pioneering idea of sparse data usage and parsimonious modeling, which promotes the actual application of MPC in buildings by guiding control-oriented model development.

Adaptive Linear Grey-Box Models for Model Predictive Controller of Residential Buildings

Model predictive control (MPC) is an advanced optimal control technique to minimize a control objective while satisfying a set of constraints and is well suited to activate the building energy flexibility. The MPC controller performance depends on the accuracy of the model prediction. Inaccurate predictions can directly lead to low control performance. Linear time-invariant (LTI) models are often used in MPC in buildings. However, LTI models do not adapt to the weather conditions varying throughout the whole space-heating season, which makes the MPC based on LTI models not perform well over a long period of time. Therefore, this study introduces an adaptive MPC where the parameters of a linear grey-box model are continuously updated in real-time. Two alternative versions of this adaptive control are investigated. The first one, called partially adaptive MPC, only updates the effective window area of the grey-box model, while the second one, called fully adaptive MPC, updates all the parameters of the grey-box model. Results show that the partially adaptive MPC is not able to deliver satisfactory prediction performance. The fully adaptive MPC shows better performance compared to the other models when implemented in a MPC, especially in avoiding thermal comfort violation.

Model predictive control under weather forecast uncertainty for HVAC systems in university buildings

In buildings, there are two yet conflicting optimization goals: 1) minimize energy use and energy cost and 2) maximize thermal comfort. Model predictive control (MPC) is an ideal control strategy to deal with the above conflicting optimization goals. However, one challenge hindering the implementation of the MPC in buildings is the weather forecast uncertainty. This study aimed to improve the performance of the MPC under weather forecast uncertainty by introducing an error model. The error model used a straightforward approach based on easily measurable and accessible data to improve the quality of weather forecast data. The proposed method was tested by simulation on a university building located in Norway, while the detailed information and measured data from this real building were used to develop and validate the building model used in this study. Results showed that the MPC with the error model was able to achieve almost the full theoretical potential of the MPC in terms of the energy cost and thermal comfort, with 3.4% of weekly energy cost saving and 73% of indoor temperature violation numbers reduction compared to a conventional rule-based controller. In contrast, due to the existence of weather forecast error and a lack of error addressing mechanism, the MPC without error model did not perform well and gave the energy cost saving of only 0.7% and the indoor temperature violation numbers even increased by 20%. Meanwhile, the results indicated the introduction of the error model always benefited the MPC performance even under the condition of the low error of weather forecast. This study may facilitate the real application of the MPC in buildings.

Model predictive control of non-domestic heating using genetic programming dynamic models

We present a novel approach to obtaining dynamic nonlinear models using genetic programming (GP) for the model predictive control (MPC) of the indoor temperatures of buildings. Currently, the large-scale adoption of MPC in buildings is economically unviable due to the time and cost involved in the design and tuning of predictive models by expert control engineers. We show that GP is able to automate this process, and have performed open-loop system identification over the data produced by an industry grade building simulator. The simulated building was subject to an amplitude modulated pseudo-random binary sequence (APRBS), which allows the collected data to be sufficiently informative to capture the underlying system dynamics under relevant operating conditions.In this initial report, we detail how we employed GP to construct the predictive model for MPC for heating a single-zone building in simulation, and report results of using this model for controlling the internal environmental conditions of the simulated single-zone building. We conclude that GP shows great promise for producing models that allow the MPC of building to achieve the desired temperature band in a single zone space.

A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings

A major challenge in applying Model Predictive Control (MPC) to building automation and control (BAC) is the development of a simplified mathematical model of the building for real-time control with fast response times. However, building models are highly complex due to nonlinearities in heat and mass transfer processes of the building itself and the accompanying air-conditioning and mechanical ventilation systems. This paper proposes a method to develop an integrated state-space model (SSM) for indoor air temperature, radiant temperature, humidity and Predicted Mean Vote (PMV) index suitable for fast real-time multiple objectives optimization. Using the model, a multi-objective MPC controller is developed and its performance is evaluated through a case study on the BCA SkyLab test bed facility in Singapore. The runtime of the MPC controller is less than 0.1 s per optimization, which is suitable for real-time BAC applications. Compared to the conventional ON/OFF control, the MPC controller can achieve up to 19.4% energy savings while keeping the PMV index within the acceptable comfort range. When the MPC controller is adjusted to be thermal-comfort-dominant that achieves a neutral PMV index at most office hours, the system can still bring about 6% in energy savings as compared to the conventional ON/OFF control.

A Short review on the use of renewable energies and model predictive control in buildings

This short review is based on an overview of the most recent works of the literature related to climatization in buildings. A total number of 40 relevant papers that have been published in the last years in prestigious international journals have been reviewed with the aim of showing the current state of the art in this field. It is very important as the new European regulations that will be applied in the next years in the construction of buildings, aiming to achieve nearly-Zero Energy Buildings (nZEBs), will require a multidisciplinary work in the different areas that affect the design of buildings. For this reason, it is relevant the envelope, the user behavior, the Heating, Ventilation and Air Conditioning applied (HVAC) and the influence of the meteorological conditions, among others. But apart from this, it would be very interesting any other alternative which helped to reach these targets. This article proposes the possibility of using Model Predictive Control (MPC) besides to Renewable Energies in order to optimize the energy management of air-conditioning in public and office buildings through a radiant floor by solar system and high-performance heat pump systems for their heating / cooling. In this paper, a short review is initially exposed where the different "Input data and information" are analyzed, to end up proposing an Operative for a Predictive Control using these Renewable Energies.

A new simplified modeling method for model predictive control in a medium-sized commercial building: A case study

Model predictive control (MPC) methods for heating, ventilation and air conditioning (HVAC) systems have been studied to improve the control accuracy and reduce energy consumption in recent years. The accuracy of the model for building thermal dynamics in MPC plays a critical role to accurately control the system. The modeling method also impacts on the real practice of MPC in buildings due to its cost and scalability. Studies have shown that an appropriate simplification of modeling procedure has minor impacts on the model accuracy, but increases the modeling efficiency. In this article, variables including weather conditions, occupancy and electricity are divided into two categories: manipulated variables and random variables. A novel two-step modeling strategy is proposed for simplifying modeling procedure and increasing model accuracy. Manipulated variables are used in step response method to develop system model. A low order system is obtained after the model simplification by observing the response curve. Random variables are used in the power spectral density (PSD) method for modeling. Transfer function is obtained through calculating the cross-power spectral density (CPSD) of the system output and input, the PSD of the input, and the ratio of CPSD and PSD. A MPC strategy with feedforward control structure is proposed to utilize the obtained dynamic characteristics of random variables and effectively compensate the errors caused by these variables. Field test in a medium-sized commercial building is implemented to evaluate the MPC strategy. The result shows that a considerable amount of energy saving is achieved through the proposed MPC.

Experimental implementation of whole building MPC with zone based thermal comfort adjustments

This paper presents an experimental implementation of a whole building model predictive control (MPC) approach for a LEED gold certified academic building. The MPC is divided into portions for morning start and daytime optimization, to allow for specialized models to be used for the varying tasks. The MPC consists of a whole building optimization for energy that is supplemented with a zone operative temperature (ZOT) comfort metric. A ZOT based metric was used to evaluate thermal comfort, as traditional air temperature metrics neglect radiative impacts on comfort. The comfort metric is employed after the energy optimization to ensure comfort is the highest priority and maintained in each zone. A final layer of occupant feedback is incorporated to allow for users to provide feedback as to their comfort level and allow for changes in the comfort temperature range for their space. A four-month experimental test period shows a 29% HVAC electric energy reduction and a 63% thermal energy reduction compared to previous years for the same time period.

Comparison of centralized and decentralized model predictive control in a building retrofit scenario

Heat transfer between apartments can challenge the positive effects of applying model predictive control (MPC) in multi-apartment buildings. This paper reports on an investigation of how the performance of two different MPC approaches – centralized and decentralized – may be affected by non-insulated and insulated partition walls between apartments. The results suggest that ignoring inter-zonal thermal effects using the less complicated decentralized approach leads to insignificant performance reductions compared to the more complicated centralized approach – especially if partition walls are insulated.

Performance comparison of occupancy count estimation and prediction with common versus dedicated sensors for building model predictive control

Model predictive control is a promising approach to optimize the operation of building systems and provide demand-response functionalities without compromising indoor comfort. The performance of model predictive control relies, among other things, on the quality of weather forecasts and building occupancy predictions. The present study compares the accuracy and computational demand of two occupancy estimation and prediction approaches suitable for building model predictive control: (1) count prediction based on indoor climate modeling and parameter estimation “using common sensors”, (2) count prediction based on data from 3D stereovision camera. The performance of the two approaches was tested in two rooms of a case study building. The results show that the method with dedicated sensors outperforms common sensors. However, if a building is not equipped with dedicated sensors, the present study shows that the common sensor method can be a satisfactory alternative to be used in model predictive control.

Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis

This paper reports the final results of the predictive building control project OptiControl-II that encompassed seven months of model predictive control (MPC) of a fully occupied Swiss office building. First, this paper provides a comprehensive literature review of experimental building MPC studies. Second, we describe the chosen control setup and modeling, the main experimental results, as well as simulation-based comparisons of MPC to industry-standard control using the EnergyPlus simulation software. Third, the costs and benefits of building MPC for cases similar to the investigated building are analyzed. In the experiments, MPC controlled the building reliably and achieved a good comfort level. The simulations suggested a significantly improved control performance in terms of energy and comfort compared with the previously installed industry-standard control strategy. However, for similar buildings and with the tools currently available, the required initial investment is likely too high to justify the deployment in everyday building projects on the basis of operating cost savings alone. Nevertheless, development investments in an MPC building automation framework and a tool for modeling building thermal dynamics together with the increasing importance of demand response and rising energy prices may push the technology into the net benefit range.

Development of a whole building model predictive control strategy for a LEED silver community college

A model predictive control strategy method is developed for a LEED silver community college building that contains an advanced common loop heat pump HVAC system. MPC is performed using persistence based climate forecasting with two scenarios examined: fixed temperature setpoint pairs; and a deadband of 1°C between the heating/cooling setpoint. EnergyPlus acts as the “real” building and is linked with R software for model predictive control via the Building Controls Virtual Test Bed. A statistical based building response model is created in R using training data from a calibrated EnergyPlus model. A brute force optimization strategy is used for solving the objective function. A piece-wise objective function maintains thermal comfort during occupied periods with a focus on reducing energy consumption during other periods. Results when using fixed setpoint pairs exhibit a 4% reduction in HVAC energy consumption, and deadband setpoints exhibit a 9% reduction in HVAC energy consumption, compared with reactive rule based control. Of interest is that the type of energy savings (thermal energy vs electricity) varies depending upon the setpoint options. The results are promising given the strict thermal comfort requirement employed, minimal search space, use of simplistic persistence forecasting, and the sophisticated HVAC system.