Data driven strategies for building performance assessment, energy usage prediction and flexibility optimization

Buildings account for a significant portion of energy consumption worldwide and are responsible for a considerable amount of carbon emissions. In the pursuit of global energy consumption reduction and carbon neutralization, effective strategies for building energy management play a pivotal role. By leveraging building operational data and data science techniques, data-driven strategies have emerged as a promising method to optimize building energy performance without the need to develop complex building models. This thesis aims to develop novel data-driven strategies to optimize building energy management with a focus on key areas, including building performance evaluation, energy usage prediction, and demand flexibility optimization. An efficient data mining strategy is first presented to assess the performance of the heating, ventilating and air conditioning system of a residential building cluster. This strategy uses Symbolic Aggregate approXimation and Kernel Density Estimation to identify typical daily load patterns and evaluate overall system performance. A customized association rule mining model was developed to discover the associations among different attributes and identify the root causes of inefficient operations. This strategy was evaluated using one-year operational data of a gound source heat pump system, effectively identifying efficient and inefficient operation patterns and revealing excessive power consumption of water pumps as the main factor impacting energy efficiency. This strategy can also be adapted for system-level performance evaluation of other energy systems with appropriate modifications. This thesis then presents a predictive modeling strategy to forecast the next-day total and peak electricity demand of a building portfolio, with the main objective to enable accurate building-level performance assessment and facilitate building demand side management. The strategy utilizes long short-term memory (LSTM) models for energy usage prediction and reinforcement learning (RL) agents to dynamically tune the parameters of LSTM models based on their prediction errors. The strategy was tested using electricity consumption data from a group of university buildings and student accommodations. The results demonstrated improved prediction accuracy for buildings with large monthly variations in electricity usage in comparison to that using LSTM models alone. The thesis further explores the development of novel data-driven strategies to optimize building operations. A framework that integrates machine learning algorithms and a domain knowledge-based expert system is proposed to improve building energy flexibility by using solar photovoltaic (PV) and battery storage systems. The framework utilizes a rule-based expert system (RBES) to maximize PV self-consumption, an RL agent to optimize grid power import for battery charging and discharging decisions, and a Classification and Regression Tree (CART) model to analyze the relationship between building energy flexibility and external variables. The performance of the framework was tested using the four-year data collected from a low-energy office building. The results showed that the integration of RL, RBES and the CART model can result in reduced electricity costs (7.0%), decreased grid power consumption (10.6%), and increased PV self-consumption (9.2%) as compared with the RBSE strategy. To further improve building energy flexibility across multiple indicators, a strategy integrating an RBES and multiple RL agents is proposed. Unlike the previous strategy, this approach uses the RBES to provide references for RL to explore optimal solutions within a reduced optimization space, improving exploration performance and avoiding unreliable decisions. The proposed strategy was tested using PV generation data and energy consumption data of a net zero energy building. The results showed that the strategy can improve the RL learning efficiency by up to 85.7% and successfully avoid sub-optimal convergence during policy learning. Compared with the rule-based expert system alone, the proposed strategy can reduce the operational cost by 5.4% and the daily peak-to-average ratio of grid power during peak hours by up to 19.2% while maintaining the same level of PV self-consumption. Compared with a model predictive control method, the strategy achieved similar decision performance in cost savings while using significantly reduced decision time. The data-driven strategies developed in this thesis hold the potential to assess the effectiveness of different building energy systems and support demand-side management within buildings.</p

A new model predictive control approach integrating physical and data-driven modelling for improved energy performance of district heating substations

District heating (DH) substations play a crucial role in ensuring the efficient and effective distribution of thermal energy necessary to provide space heating for buildings. However, optimizing their operation for energy savings while still ensuring indoor comfort poses significant challenges due to the complex dynamics of building demand and the inertia of building envelopes. To address these challenges, this study introduces a novel model predictive control (MPC) approach that combines a reduced-order physical model with a machine learning-based data-driven model to jointly optimize the operation parameters of a DH substation. In this approach, a reduced-order physical model is first used to capture essential operational principles and energy behaviors of the DH substations and generate candidate solutions for the control of the DH substations. Then, a data-driven model is constructed by integrating a Long Short-Term Memory model and a Back-propagation Neural Network, leveraging historical operational data of the DH substation concerned. The data-driven model is further formulated into a data-driven MPC framework to identify optimal control solutions from all candidates provided by the physical model. To evaluate the proposed approach, a data-driven surrogate model is developed using real operational data. Comparative analysis against the original fuzzy rule-based control strategy and a pure data-driven strategy demonstrates a substantial reduction in heat consumption of 4.77% and 19.47%, respectively. Moreover, compared with using a reduced-order physical model alone, this approach achieves additional benefits in reducing the energy consumption of the DH substation and minimizing indoor temperature fluctuations within the end-users

Table_1_Association Between Lipid Accumulation Product and Cognitive Function in Hypertensive Patients With Normal Weight: Insight From the China H-type Hypertension Registry Study.XLS

BackgroundHypertension is a major cardiovascular risk factor for cognitive impairment. Lipid accumulation product (LAP), an index that represents fat overaccumulation in the body, has been shown to be associated with cardiovascular disease. Nevertheless, the relationship between LAP and cognitive function in hypertensive patients with normal weight has been infrequently studied.ObjectiveThis study aimed to assess the relationship between LAP and cognitive function in hypertensive patients with normal weight.MethodsThis study included 5,542 Chinese hypertensive patients with normal weight. Cognitive function was evaluated using the Mini-Mental State Examination (MMSE). The relationship between LAP and MMSE scores was evaluated using multiple linear regression.ResultsThe mean age of the participants was 64.8 ± 9.3 years, and 2,700 were men (48.7%). The mean MMSE score was 24.5 ± 5.1 in men and 19.2 ± 6.5 in women. The mean LAP was 26.2 ± 25.5 in men and 42.5 ± 34 in women. Log10-LAP showed a significant positive association with MMSE score (men: β = 0.69, 95% CI 0.14–1.24, p = 0.015; women: β = 1.03, 95% CI 0.16–1.90, p = 0.020). When LAP was divided into 3 groups according to tertiles, participants in the third LAP tertile had higher MMSE scores for both men (p for trend = 0.04) and women (p for trend = 0.015).ConclusionLAP showed an independent positive association with MMSE in Chinese hypertensive patients with normal weight.</p

Additional file 1 of Association of marital status with cognitive function in Chinese hypertensive patients: a cross-sectional study

Additional file 1

DataSheet_1_Association between remnant cholesterol and chronic kidney disease in Chinese hypertensive patients.docx

BackgroundRemnant cholesterol (RC) and chronic kidney disease (CKD) have not been definitively linked in individuals with different characteristics. This study aims to investigate the relationship between serum RC level and CKD and examine possible effect modifiers in Chinese patients with hypertension.MethodsOur study is based on the Chinese H-type Hypertension Project, which is an observational registry study conducted in real-world settings. The outcome was CKD, defined as an estimated glomerular filtration rate of less than 60 ml/min·1.73 m2. Multivariate logistic regression and smooth curve fitting were used to analyze the association between RC and CKD. Subgroup analyses were subsequently conducted to examine the effects of other variables.ResultsThe mean age of the 13,024 patients with hypertension at baseline was 63.8 ± 9.4 years, and 46.8% were male. A conspicuous linear positive association was observed between RC level and CKD (per SD increment; odds ratio [OR], 1.15; 95% confidence interval [CI], 1.08–1.23). Compared with the lowest quartile group of RC, the risk of CKD was 53% higher (OR, 1.53; 95% CI, 1.26–1.86) in the highest quartile group. Furthermore, a stronger positive association between RC level and CKD was found among participants with a higher body mass index (BMI 2; P-interaction = 0.034) or current non-smokers (smoker vs. non-smoker; P-interaction = 0.024).ConclusionsAmong Chinese adults with hypertension, RC level was positively associated with CKD, particularly in those with a BMI of ≥24 kg/m2 and current non-smokers. These findings may help improve lipid management regimens in patients with hypertension.</p