Data Predictive Control for Peak Power Reduction

Jain, Achin; Mangharam, Rahul; Behl, Madhur

doi:10.1145/2993422.2993582

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…Step 2: Linear regression models are trained in the leaves (or terminal nodes) of the trees which are function of only X c . We have validated this linear model assumption in [10]. As we shall see in Sec.…”

Section: A Separation Of Variablesmentioning

confidence: 65%

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Data predictive control using regression trees and ensemble learning

Jain

Smarra

Mangharam

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Decisions on how to best operate large complex plants such as natural gas processing, oil refineries, and energy efficient buildings are becoming ever so complex that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC, is the cost, time, and effort associated with learning first-principles dynamical models of the underlying physical system. An alternative approach is to employ learning algorithms to build black-box models which rely only on real-time data from the sensors. Machine learning is widely used for regression and classification, but thus far data-driven models have not been used for closed-loop control. We present novel Data Predictive Control (DPC) algorithms that use Regression Trees and Random Forests for receding horizon control. We demonstrate the strength of our approach with a case study on a bilinear building model identified using real weather data and sensor measurements. In a one-to-one comparison, we show that DPC explains 70\% variation in the MPC controller. We further apply DPC to a large scale multi-story EnergyPlus building model to curtail total power consumption in a Demand Response setting. In such cases, when the model-based controllers fail due to modeling cost, complexity and scalability, our results show that DPC curtails the desired power usage with high confidence.

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Section: A Separation Of Variablesmentioning

confidence: 65%

“…In our previous work [9], [10], we introduced the concept of DPC for receding horizon control. This work has the following contributions.…”

Section: Introductionmentioning

confidence: 99%

Data predictive control using regression trees and ensemble learning

Jain

Smarra

Mangharam

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

Self Cite

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“…To overcome this complexity above, the regression treebased approaches were employed in the literature to develop data-driven predictive models. Authors in [30,31] developed RT and random forest for building control in different settings. However, the simulation results demonstrated that these models were trapped in limitations due to overfitting and high variance [5].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture

Qin

Wang

et al. 2020

Wireless Communications and Mobile Computing

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Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon. However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive. Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling. This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture. Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation. Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate. The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity. The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD). The proposed strategies are demonstrated both in residential and office buildings. The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.

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“…Therefore, developing a learning based approach for the optimal DR strategies and the relevant model can be learned through the historical data, which is urgent and attractive. Some recent literature has paid attention to the study based on the learning method [18][19][20][21][22][23][24][25]. For example, in [18], a model based control with a regression trees method was exploited for optimal DR strategies for large commercial buildings.…”

Section: Introductionmentioning

confidence: 99%

A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort

Han

Wang

et al. 2018

Energies

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Demand response (DR) has become an effective and critical method for obtaining better savings on energy consumption and cost. Buildings are the potential demand response resource since they contribute nearly 50% of the electricity usage. Currently, more DR applications for buildings were rule-based or utilized a simplified physical model. These methods may not fully embody the interaction among various features in the building. Based on the tree model, this paper presents a novel model based control with a random forest (MBCRF) learning algorithm for the demand response of commercial buildings. The baseline load of demand response and optimal control strategies are solved to respond to the DR request signals during peak load periods. Energy cost saving of the building is achieved and occupant’s thermal comfort is guaranteed simultaneously. A linguistic if-then rules-based optimal feature selection framework is also utilized to redefine the training and test set. Numerical testing results of the Pennsylvania-Jersey-Maryland (PJM) electricity market and Research and Support Facility (RSF) building show that the load forecasting error is as low as 1.28%. The peak load reduction is up to 40 kW, which achieves a 15% curtailment and outperforms rule-based DR by 5.6%.

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Data Predictive Control for Peak Power Reduction

Cited by 12 publications

References 19 publications

Data predictive control using regression trees and ensemble learning

Data predictive control using regression trees and ensemble learning

Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture

A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort

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