Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems

Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system’s dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy. We also present DR-Advisor, a data-driven demand response recommender system for the building\u27s facilities manager which provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We develop a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based demand response methods for a large DoE commercial reference building and leads to a significant amount of load curtailment (of 380kW) and over $45,000 in savings which is 37.9% of the summer energy bill for the building. The performance of DR-Advisor is also evaluated for 8 buildings on Penn\u27s campus; where it achieves 92.8% to 98.9% prediction accuracy. We also compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE\u27s benchmarking data-set for energy prediction

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Energy use in Urban Transport sector within the Sustainable Energy Action Plans (SEAPs) of three Italian Big Cities

Promising Renewable Energy solutions could be installed in cities, but they require specific morphological conditions as well as architectural integration. Transport sector is still neglected from a strong policy initiative. A first attempt along with a defined framework to attract economic resources as well as interested stakeholders is the Covenant of Mayors (CoM). Within this agreement, the Municipality has to design a plan, the so-called Sustainable Energy Action Plan (SEAP). The plan must contain a clear outline of the strategy and relative actions to be taken by the local authority to reach its commitments in 2020, in terms of sustainability goals set by EU 20-20-20. The aim of this paper is to discuss and evaluate the differences of fuel usage and transport sector interaction in Italian urban scenarios, taking into account geographical and morphological constraints, and to compare the forecasts for 2020 and 2030scenarios, in accordance with European and National laws in force

Understanding and controlling the ingress of driven rain through exposed, solid wall masonry structures

Long term performance of historic buildings can be affected by many environmental factors, some of which become more apparent as the competence of the fabric deteriorates. Many tall historic buildings suffer from water ingress when exposed to driving rain conditions, particularly church towers in the south west of England. It is important to recognise that leakage can occur not only through flaws in the roof of a building but also through significant thicknesses of solid masonry. Identification of the most appropriate intervention requires an understanding of the way in which water might enter the structure and the assessment of potential repair options. While the full work schedule used an integrated assessment involving laboratory, field and archival work to assess the repairs which might be undertaken on these solid wall structures, this paper focuses on the laboratory work done to inform the writing of a Technical Advice Note on the effects of wind driven rain and moisture movement in historic structures (English Heritage, 2012). The laboratory work showed that grouting and rendering was effective at reducing water penetration without retarding drying rates, but that use of internal plastering also had a very beneficial effect

Cooling load forecasting-based predictive optimisation for chiller plants

Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Instead of minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced

Assessment of construction cost reduction of nearly zero energy dwellings in a life cycle perspective

Concerning Nearly Zero Energy Buildings, it is important to guarantee energy efficiency, thermal comfort and indoor environmental quality, while keeping construction and operational costs low. In this framework, this paper explores the efficacy of applying different scenarios, for reducing construction costs of new nearly zero energy multi-family houses in a life cycle perspective. Conversely to the standard cost-optimal approach, a real Italian case study building was chosen. Alternative and unconventional combinations of solutions for envelope and technical systems were adopted. Calculations were performed in two Italian cities (Rome and Turin). Three types of analysis were developed thermal comfort, energy performance and financial calculation. Results of the thermal analysis show that the installation of active cooling to prevent summer overheating can be avoided by applying low-cost passive strategies. All the proposed low-cost scenarios (4 alternative scenarios in Rome and 5 in Turin)reached the highest grade of energy performance, with a reduction of the non-renewable primary energy consumption up to 46% compared to the base case in Rome and 18% in Turin. From the economic perspective, all the scenarios in the two climate zones allow both reductions in the construction costs, up to 26% in Rome and 15% in Turin, and a Net Present Value after 50 years up to 163 €/m2 in Rome and 158 €/m2 in Turin