An Optimization Strategy for Scheduling Various Thermal Energy Storage Technologies in Office Buildings Connected to Smart Grid

AbstractAn optimization strategy for scheduling various thermal energy storage capacities in an office building is investigated. An activated building wall (building thermal mass), a phase change material (PCM) tank, a hot water (HW) tank and a thermochemical material (TCM) storage are simulated and their charging and discharging behavior are optimized. Therefore, a case study is performed using a very simplified room model (office) and typical weather data for the Netherlands. To model the storage's scheduling behavior for control and optimization, a resistance capacitance (RC) network is applied. The RC network represents the critical energy storage parameters for optimization and control. The minimization of electricity costs is defined as optimization objective towards the Smart Grid. Cost saving potentials up to 12.5% are calculated using an electrical heat pump and a solar collector for heating the room and charging the thermal energy storage capacities

Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings

As with many other sectors, to improve the energy performance and energy neutrality requirements of individual buildings and groups of buildings, built environment is also making use of machine learning for improved energy demand predictions. The goal of achieving energy neutrality through maximized use of on-site produced renewable energy and attaining optimal level of energy performance at building-cluster level requires reliable short term (resolution shorter than one day) energy demand predictions. However, the prediction and analysis of the energy performance of buildings is still focused on the individual building level and not on small neighborhood scale or building clusters. In a smart grid context, to better understand electricity consumption at different spatial levels, prediction should be at both individual as well as at building-cluster levels, especially for neighborhoods with definite boundaries (such as universities, hospitals). Therefore, in this paper, using data from 47 commercial buildings, a number of machine learning algorithms were evaluated to predict the electricity demand at individual building level and aggregated level in hourly intervals. Predicting at hourly granularity is important to understand short-term dynamics, yet most of the neighborhood scale studies are limited to yearly, monthly, weekly, or daily data resolutions. Two years of data were used in training the model and the prediction was performed using another year of untrained data. Learning algorithms such as; boosted-tree, random forest, SVM-linear, quadratic, cubic, fine-Gaussian as well as ANN were all analysed and tested for predicting the electricity demand of individual and groups of buildings. The results showed that boosted-tree, random forest, and ANN provided the best outcomes for prediction at hourly granularity when metrics such as computational time and error accuracy are compared.</p

A bottom-up framework for analysing city-scale energy data using high dimension reduction techniques

Worldwide cities are becoming more sustainable and are being monitored using data collection techniques at various geographical levels. Given the growing volume of data, there is a need to identify challenges associated with the processing, visualization, and analysis of the generated data from an urban scale. This study proposes a framework to investigate the capabilities of dimensionality reduction techniques (t-SNE, and UMAP) applied to city-scale data to identify key features of high consumption and generation areas based on building characteristics. The analysis is performed on measured data from 2735 postcodes consisting of 72000 households/buildings from a city in the Netherlands. The evaluation results showed that the UMAP's algorithm mean sigma quickly approaches a threshold of 0.6 at n_neighbor values of 50 and the low dimensional shape does not change with increasing values. Whereas the t-SNE's mean sigma value increases continuously with the increasing perplexity value, implying that t-SNE is significantly more sensitive to the perplexity parameter. The UMAP algorithm was used to extract information about the high photovoltaic generation and consumption regions. The proposed framework will assist grid operators and energy planners in extracting information from energy consumption data at the neighbourhood level by utilizing high dimensional reduction techniques

Are building users prepared for energy flexible buildings—A large-scale survey in the Netherlands

Building energy flexibility might play a crucial role in demand side management for integrating intermittent renewables into smart grids. The potential of building energy flexibility depends not only on the physical characteristics of a building but also on occupant behaviour in the building. Building users will have to adopt smart technologies and to change their daily energy use behaviours or routines, if energy flexibility is to be achieved. The willingness of users to make changes will determine how much demand flexibility can be achieved in buildings and whether energy flexible buildings can be realized. This will have a considerable impact on the transition to smart grids. This study is thus to assess the perception of smart grids and energy flexible buildings by building users, and their readiness for them on a large scale. We attempted to identify the key characteristics of the ideal user of flexible buildings. A questionnaire was designed and administered as an online survey in the Netherlands. The questionnaire consisted of questions about the sociodemographic characteristics of the current users, house type, household composition, current energy use behaviour, willingness to use smart technologies, and willingness to change energy use behaviour. The survey was completed by 835 respondents, of which 785 (94%) were considered to have provided a genuine response. Our analysis showed that the concept of smart grids is an unfamiliar one, as more than 60% of the respondents had never heard of smart grids. However, unfamiliarity with smart grids increased with age, and half of the respondents aged 20–29 years old were aware of the concept. Monetary incentives were identified as the biggest motivating factor for adoption of smart grid technologies. It was also found that people would be most in favour of acquiring smart dishwashers (65% of the respondents) and refrigerator/freezers (60%). Statistical analysis shows that people who are willing to use smart technologies are also willing to change their behaviour, and can thus be categorised as potentially flexible building users. Given certain assumptions, 11% of the respondents were found to be potentially flexible building users. To encourage people to be prepared for energy flexible buildings, awareness of smart grids will have to be increased, and the adoption of smart technologies may have to be promoted by providing incentives such as financial rewards

Neural network based predictive control of personalized heating systems

The aim of a personalized heating system is to provide a desirable microclimate for each individual when heating is needed. In this paper, we present a method based on machine learning algorithms for generation of predictive models for use in control of personalized heating systems. Data was collected from two individual test subjects in an experiment that consisted of 14 sessions per test subject with each session lasting 4 h. A dynamic recurrent nonlinear autoregressive neural network with exogenous inputs (NARX) was used for developing the models for the prediction of personalized heating settings. The models for subjects A and B were tested with the data that was not used in creating the neural network (unseen data) to evaluate the accuracy of the prediction. Trained NARX showed good performance when tested with the unseen data, with no sign of overfitting. For model A, the optimal network was with 12 hidden neurons with root mean square error equal to 0.043 and Pearson correlation coefficient equal to 0.994. The best result for model B was obtained with a neural network with 16 hidden neurons with root mean square error equal to 0.049 and Pearson correlation coefficient equal to 0.966. In addition to the neural network models, several other machine learning algorithms were tested. Furthermore, the models were on-line tested and the results showed that the test subjects were satisfied with the heating settings that were automatically controlled using the models. Tests with automatic control showed that both test subjects felt comfortable throughout the tests and test subjects expressed their satisfaction with the automatic control

Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads

Optimal decision-making compels us to anticipate the future at different horizons. However, in many domains connecting together predictions from multiple time horizons and abstractions levels across their organization becomes all the more important, else decision-makers would be planning using separate and possibly conflicting views of the future. This notably applies to smart grid operation. To optimally manage energy flows in such systems, accurate and coherent predictions must be made across varying aggregation levels and horizons. With this work, we propose a novel multi-dimensional hierarchical forecasting method built upon structurally-informed machine-learning regressors and established hierarchical reconciliation taxonomy. A generic formulation of multi-dimensional hierarchies, reconciling spatial and temporal hierarchies under a common frame is initially defined. Next, a coherency-informed hierarchical learner is developed built upon a custom loss function leveraging optimal reconciliation methods. Coherency of the produced hierarchical forecasts is then secured using similar reconciliation technics. The outcome is a unified and coherent forecast across all examined dimensions. The method is evaluated on two different case studies to predict building electrical loads across spatial, temporal, and spatio-temporal hierarchies. Although the regressor natively profits from computationally efficient learning, results displayed disparate performances, demonstrating the value of hierarchical-coherent learning in only one setting. Yet, supported by a comprehensive result analysis, existing obstacles were clearly delineated, presenting distinct pathways for future work. Overall, the paper expands and unites traditionally disjointed hierarchical forecasting methods providing a fertile route toward a novel generation of forecasting regressors

Development and evaluation of a building integrated aquifer thermal storage model

An aquifer thermal energy storage (ATES) in combination with a heat pump is an excellent way to reduce the net energy usage of buildings. The use of ATES has been demonstrated to have the potential to provide a reduction of between 20 and 40% in the cooling and heating energy use of buildings. ATES systems are however a complex system to analyse as a number of ground conditions influence heat losses within the aquifer. ATES is also not confined from the sides and is therefore vulnerable to heat losses through conduction, advection and dispersion. The analyses of ATES system is even further complicated when the dynamic of a building is considered. When connected to a building, the temperature in the aquifer is influenced by the amount of heat exchange with the varying building load. Given the energy saving potentials of ATES systems in building operation, detailed understanding of the influence of buildings on the ATES systems and vice versa would facilitate improved operation and efficiency of ATES and building coupled systems. Therefore, taking into account the variations in the building and below ground conditions, there is the need for the development of a model that can potentially handle the dynamics on both sides. Finite element and finite volume methods are frequently used in the development of ATES models and proven as adequate tools for modelling complex ground conditions, however, most developed ATES models are often analysed independent of the building. Therefore, in this study, an ATES model that also integrates building dynamics is developed using the finite element method (FEM). The developed model was validated using data from an ATES and building in the Netherlands. The developed model was shown to have an absolute mean error of 0.17 °C and 0.12 °C for the cold and warm wells respectively

Personalized heating – Comparison of heaters and control modes

AbstractPersonalized conditioning systems represent a promising solution to two major challenges in building industry – high energy consumption of the buildings and still only mediocre thermal comfort. These systems create a microenvironment adapted for each user. Therefore, individual demands for thermal comfort can be met and energy can be saved due to higher effectiveness compared to the traditional HVAC systems. This study investigates two aspects of personalized heating – effectiveness of different heaters and impact of different control modes. Personalized heating system consisting of a heated chair, a heated desk mat, and a heated floor mat was tested with 13 test subjects in a climate chamber under operative temperature of 18 °C. The heaters were tested separately and in combination as user controlled. Furthermore, the complete system was tested with fixed setting and automatic control using hand skin temperature as a control signal. The heated chair and the heated desk mat as well as the complete system significantly improved thermal comfort, while the heated chair was found to the most effective heater. The automatic control mode could provide the same level of thermal comfort as user control in this study

Bedroom ventilation performance in daycare centers under three typical ventilation strategies

With increasing reliance on daycare centers for early childhood development, ensuring healthy environments in semi-enclosed baby beds for napping is crucial, given infants' vulnerability to air pollutants and their inability to control surroundings. Despite concerns about indoor air quality, research on bed-level ventilation conditions remains scarce. This study investigates the performance of three ventilation strategies (mixing ventilation (MV), displacement ventilation (DV), and personalized ventilation (PV)) in enhancing air quality at bed level in daycare center bedrooms. Using a full-scale setup representing a typical Dutch daycare bedroom, ventilation performance was evaluated by examining CO2 dispersion and inhalation for 12 breathing thermal baby models sleeping in 12 beds, considering two sleep positions and ventilation rates. A total of 58 strategically located CO2 sensors enabled a thorough understanding of CO2 levels at inhalation and both the bed and room scales. The findings reveal the superior performance of PV, followed by DV and MV, with significantly different inhaled CO2 concentrations per baby: 1713 ppm (MV), 1104 ppm (DV), and 801 ppm (PV), though the mean in-bed values differed by less than 20 ppm among the modes. Thus, assessing ventilation performance of various ventilation strategies necessitates examining inhaled air quality. Sleep positions and ventilation rates significantly influenced MV and DV modes' performance. Importantly, PV demonstrated energy-saving potential by achieving comparable inhaled air quality at lower ventilation rates. These findings have practical implications for designing occupant-centric ventilation systems in daycare center bedrooms and effective CO2 monitoring in semi-enclosed spaces.</p