Smart Heating Research Articles

Smart heating in collaborative and reasoning-enabled housing units

In aiming to create added intelligence for Things interfacing with IoT principles in the form of Services, Smart Heating Management is consistently a field of promising research. In the currently demonstrated work, an approach will be described for an operational software framework of modular components that can create intelligence in Housing Units as those presented in our scenario. The framework is based on the lightweight Case Based Reasoning approach and principles in order to describe the Problem and Solution of heating management, as well as to extract knowledge from generic historical data. Collaboration between houses is included through the sharing of anonymized high level problem-solution data, as an instantiation of the same social learning principles that govern human behavior and enable Knowledge diffusion. The approach is validated through historical data acquired in two time periods from the Camden (London) community residencies and demonstrates an average of 22% savings in boiler active time. Knowledge sharing is also analyzed along with the benefits and pitfalls it might produce.

Vulnerability Analysis of A Smart Heating System

Abstract The opportunities offered by the smart city and the smart home to create livelier cities and homes in cities of increasing populations. [1] Meanwhile, there may be more problems which the average user does not yet think about. Focusing on these issues will be of utmost importance for information security and IT security, as the development of technology may not be able to keep up with a society such technologies are used in. On a theoretical and practical level, I examine an intelligent heating system for IT vulnerability, as well as some cases of attacks against IoT in recent years.

Mathematical modelling of smart solar heating system with the deployment of borehole thermal energy storage to increase renewable heat share in Dundee, UK.

The chance of achieving 12% of renewable energy in heating demand by 2020 has gone, as the current RES share is almost the half of this target. The UK struggle to address this deficit, but it still needs more deployment of renewables. Solar energy has proved its viability and is going to play a significant role in increasing the share of renewable heat in domestic heating demand. What makes it more promising than before is that scientists have dealt with many challenges that weaken its growth. Namely, seasonal thermal energy storage that used to solve the mismatch between surplus solar energy in summer and high heating demand in winter. This study investigates the performance of a fully electrified smart heating system [1] that combines solar photovoltaics, borehole thermal energy storage and heat pumps, and compare the results of different sizes communities in Dundee to track the behaviour of BTES and the whole system. Different Solar Fraction and RES Percentage have been achieved with varying demands of heating and different sizes communities.

Real-time grid optimisation through digitalisation – results of the STORM project

The STORM controller is a platform for operational demand side management and has been developed since 2015 as part of the Horizon 2020 project STORM. The controller has been developed using state-of-the-art solutions based on machine learning and applied artificial intelligence, and it has already spawned results now being used in commercial smart heat grid projects throughout Europe. The results show significant benefits relating to peak load management, distribution balancing and market interaction mechanisms.

A Layered Software Architecture for a Flexible and Smart Organic Rankine Cycle (ORC) Turbine – Solutions and Case Study

Intelligent Technical Systems are the consequent extension of the concept of Mechatronic Systems into the world of software-intensive, networked systems. The concept enhances the functionality and variability of physical (or electro-mechanical) technical systems by adding embedded electronics and software. This adds degrees of freedom (DoF) and controllability to the technical systems. It enhances the effectivity of integrating such systems into distributed infrastructures (e.g. smart grids) via a public network like the internet. Therefore, it enables them for an effective integration into the internet-of-things (IoT) since such Intelligent Technical Systems can add and draw significant functionality and optimisation potential from networking with other systems. They can become an integral part of a distributed system which is coupled via the IoT. Nevertheless, it is important to leverage between local control and distributed control carefully according to the requirements of the technical application. A smart heating grid is an example of a distributed cyber-physical system (CPS) which can be coupled via IoT. Such systems include components like block heat and power plants which produce electricity (fed into the public smart grid) and heat. Smart heating grids benefit from components which can convert heat into electricity in a flexible and controllable way and which can change fast from heat provision to electricity provision and vice-versa. An Organic Rankine Cycle (ORC) Turbine is such a component since it converts exhaust heat into electricity. This takes heat out of the heating grid and puts electricity into the electricity grid instead. Making such an ORC turbine intelligent means optimizing it for the usage in a smart heating grid. The challenge is to design a control software architecture which allows coupling via IoT on the required interaction level of the distributed system while guaranteeing a safe operation on the local level. The article is an extended version of a previous article at the ICIST conference. It presents the software architecture of an ORC turbine based on the architecture of the Operator-Controller-Module (OCM). Compared to the previous publication it provides a more in-depth presentation of the prototype implementation of the ORC turbine system. The OCM provides an architecture pattern which allows a seamless integration into a smart heating grid based on an IoT infrastructure while enabling maximum flexibility and efficiency of the local functionality of the turbine.DOI: http://dx.doi.org/10.5755/j01.itc.47.2.19681

ALOS: Automatic learning of an occupancy schedule based on a new prediction model for a smart heating management system

In our day and age, lowering energy consumption in buildings is a must. Smart-buildings will provide the answer if and when they can adjust the required indoor temperature to the occupancy. Developing an occupancy model that forecasts the time of arrival and departure is therefore mandatory. Our article deals with the occupancy prediction model of a building meant for an inteligent heating management system. The prediction also integrates short and long duration of occupation/unoccupation. ALOS is based on an unsupervised clustering method (to classify the events ‘departure’ and ‘arrival’) and on the EM (Expectation Maximisation) algorithm with a new mixture model to determine short and long duration of the events. While most previous studies focused on either the residential or the tertiary building, our approach predicts occupancy in both types of buildings. In order to demonstrate the efficiency of our approach, it was tested on real occupancy datasets (familly consisting of 4 people and elderly person living alone). The results indicate that ALOS achieves excellent average prediction accuracies, notebaly from 80% up to 90%, which makes it efficient and provides easy implementation. Finally, a major strength of the ALOS method is that it only needs just under a week to integrate a change of the occupants' habits.

Fast Adaptive Thermal Buffering by a Passive Open Shell Based on Transformation Thermodynamics

AbstractBy using transformation thermodynamics, an open, adaptive thermal buffer shell (TBS) is designed and experimentally implemented with natural materials (layered copper and expanded polystyrene [EPS]). The TBS can store excessive heat flux quickly in the shell and release it slowly when it is needed, which also has some distinctive features: first it is a buffer shell that can avoid external extremes in temperature (either high or low) and also provide a relatively stable temperature for the interior of the shell when the temperature outside the shell has some fluctuations; second the TBS is not thermally insulated (with holes connected to the outside world) and the buffering effect of the TBS originates from the heat recycling mechanics, not thermal insulation; third the TBS has a short response time and the temperature inside the shell can be quickly adjusted according to the average temperature outside the shell; fourth the TBS is a passive device. Unlike traditional air conditioning, the TBS can work without an external power supply. The TBS will have many applications in energy recycling systems, smart heat controlling systems, and passive heat self‐adaptation systems. Numerical simulations and experimental demonstrations are given to verify the function of the TBS.

Co-simulation of cyber-physical systems using a DEVS wrapping strategy in the MECSYCO middleware

Most modeling and simulation (M&S) questions about cyber-physical systems (CPSs) require expert skills belonging to different scientific fields. The challenges are then to integrate each domain’s tools (formalism and simulation software) within the rigorous framework of M&S process. To answer this issue, we give the specifications of the Multi-agent Environment for Complex-SYstem CO-simulation (MECSYCO) middleware which enables to interconnect several pre-existing and heterogeneous M&S tools, so they can simulate a whole CPS together. The middleware performs the co-simulation in a parallel, decentralized, and distributable fashion thanks to its modular multi-agent architecture. In order to rigorously integrate tools that use different formalisms, the co-simulation engine of MECSYCO is based on the discrete event system specification (DEVS). The central idea of MECSYCO is to use a DEVS wrapping strategy to integrate each tool into the middleware. Thus, heterogeneous tools can be homogeneously co-simulated in the form of a DEVS system. By using DEVS, MECSYCO benefits from the numerous scientific works which have demonstrated the integrative power of this formalism and give crucial guidelines to rigorously design wrappers. We demonstrate that our discrete framework can integrate a vast amount of continuous M&S tools by wrapping the Functional Mockup Interface (FMI) standard. To this end, we take advantage of DEVS efforts of the literature (namely, the DEV&DESS hybrid formalism and Quantized State System (QSS) solvers) to design DEVS wrappers for Functional Mockup Unit (FMU) components. As a side-effect, this wrapping is not restricted to MECSYCO but can be applied in any DEVS-based platform. We evaluate MECSYCO with the proof of concept of a smart heating use case, where we co-simulate non-DEVS-centric M&S tools.

The role of knowledge about user behaviour in demand response management of domestic hot water usage

Load balancing is an important topic in smart grid systems. Dynamic pricing is a common approach to achieve a better balance between renewable energy production and energy usage. This assumes that individual households adapt their energy usage patterns based on energy prices. However, the actual behaviour of consumers in a household is an uncertain factor that might influence the effectiveness of pricing strategies. In this paper, we investigate to what extent knowledge about actual user behaviour can contribute to local optimization of energy usage. We use simulations to study whether a smart heating system that applies a pre-heating strategy for domestic water during periods of low prices can benefit from good predictions of the user behaviour, in financial terms or in terms of energy saving. Also, we use the simulations to investigate the effect of different goal temperatures for the pre-heating strategy. The results show that pre-heating does not make a difference with respect to the energy efficiency, but that during cold months, pre-heating can result in a financial benefit. In addition, we calculate what certainty about the user behaviour is needed to be able to effectively use pre-heating during the warmer summer month. These results can help to design residential energy optimization systems.

A Comfort-Based Approach to Smart Heating and Air Conditioning

In this article, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation, and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set-point temperature. Being built using broad population statistics, these models generally fail to represent individual users’ preferences, resulting in poor estimates of the users’ preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This personalised thermal comfort model uses a Bayesian network to learn from a user’s feedback, allowing it to adapt to the users’ individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 dataset and data collected in a separate deployment by us, we show that our model is consistently 13.2% to 25.8% more accurate than current models and how using our alternative comfort scale can increase our model’s accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3% to 13.5% while decreasing user discomfort by 24.8% simultaneously.

Increased self-consumption and grid flexibility of PV and heat pump systems with thermal and electrical storage

The growing share of solar and wind energy sources requires increased efforts to ensure stability of the electrical grid and reliability of energy supply. Photovoltaic (PV) systems in combination with heat pumps (HP), local thermal (TES) and electrical energy storage offer the possibility to achieve higher PV self-consumption or services for stabilizing the electricity grid. Smart heat pump systems that are used in single family homes in combination with photovoltaics and local storages are tested with a hardware-in-the-loop approach on the test bench. Different system designs and control options are analyzed with system simulations (TRNSYS). The results are discussed with respect to the economical boundary conditions defined by the electricity market prices, in particular the difference between the purchase price and the feed-in tariff, with a focus on the current situation in Switzerland. The benefits of decreasing electricity purchase from the grid are compared with current prices of both electrical and thermal storages.

Exploring zero-training algorithms for occupancy detection based on smart meter measurements

Detecting the occupancy in households is becoming increasingly important for enabling context-aware applications in smart homes. For example, smart heating systems, which aim at optimising the heating energy, often use the occupancy to determine when to heat the home. The occupancy schedule of a household can be inferred from the electricity consumption, as its changes indicate the presence or absence of inhabitants. As smart meters become more widespread, the real-time electricity consumption of households is often available in digital form. For such data, supervised classifiers are typically employed as occupancy detection mechanisms. However, these have to be trained on data labelled with the occupancy ground truth. Labelling occupancy data requires a high effort, sometimes it even may be impossible, making it difficult to apply these methods in real-world settings. Alternatively, one could use unsupervised classifiers, which do not require any labelled data for training. In this work, we introduce and explain several unsupervised occupancy detection algorithms. We evaluate these algorithms by applying them to three publicly available datasets with ground truth occupancy data, and compare them to one existing unsupervised classifier and several supervised classifiers. Two unsupervised algorithms perform the best and we find that the unsupervised classifiers outperform the supervised ones we compared to. Interestingly, we achieve a similar classification performance on coarse-grained aggregated datasets and their fine-grained counterparts.

Legal governance of smart heat infrastructure development under modes of liberalization; how to analyze and overcome deadlocks in heat projects

The background of this article is how the challenge to accomplish a sustainable energy transition—in service of various objectives, such as environmental and geopolitical concerns—has recently brought the development of smart heat infrastructures to the public agenda. Especially in Metropolitan regions, with more closely knit combinations of urban functions, establishing smart heat infrastructures and possibly connections between infrastructures, to form a heat infrastructure, is regarded as a serious option for developing an alternative energy market next to electricity and natural gas.Orchestration seems key to overcome a stalemate in the realization of heat infrastructures (context) through concerted energy infrastructure planning. This conceptual article aims to support such orchestration by presenting a legal governance typology for heat infrastructures that combines the nature of the infrastructure-regime with the complexity of the infrastructure-functionality. Thus, four ideal-type positions are defined, each with a particular dominant/lead actor position, as standard types of legal governance orchestration, with many in between hybrid positions. Orchestration is further discussed in the context of levels of action situations, contrasting top-down and bottom-up mechanisms influencing orchestration in collective choice towards establishing heat infrastructures.In the conceptualization of this typology in context, the article addresses the example of (considered) changes in Dutch Heat Energy Policy to support multilateral public orchestration, with the ambition of especially promoting the establishment of smart, open heat infrastructures, and avoiding failure of uni- or multilateral private orchestration. The proposed model is presented as a first step towards developing a policy-implementation tool to support the development of smart heat infrastructures.

Power-to-Gas integration in the Transition towards Future Urban Energy Systems

Temperature levels play a key role in the thermal energy demand of urban contexts affecting their associated primary energy consumption and Renewable Energy Fraction. A Smart Heating strategy accounts for those supply features requiring new solutions to be effectively renewable and to solve the RES capacity firming. Power-to-Gas (P2G) is the way to decarbonize the energy supply chain as fraction of Hybrid fuels, combination of fossil ones and Renewable Hydrogen, as immediate responsive storage solution. While, Power-To-Heat is conceived as the strategy to modernize the high and medium temperature heating systems by electricity-driven machines to switch from Fuel-to-Heat to Electricity-to-Heat solutions. The authors investigated on different urban energy scenarios at RES share increase from 25% up to 50% in the energy mix to highlight strengths and weaknesses of the P2G applications. Primary Energy Consumption was chosen as the objective function. Three Reference Cities were chosen as reference scenarios. Moreover, the analytical models of P2G was designed and implemented in the reference energy system. The results of the twelve scenarios, four for each Reference City were evaluated in terms of amount of Renewable Heat delivered. Finally, the interaction between P2G and renewable heat production was evaluated.

6-2-1 スマートヒートグリッドの既成市街地における適応多様性に関する研究（第4報）

6-2-1 スマートヒートグリッドの既成市街地における適応多様性に関する研究（第4報）

Development of a Solid Oxide Electrolysis Stack Able to Operate at High Steam Conversion Rate and Integration into a SOE System

Solid Oxide Electrolysis (SOE) technology is considered to be a highly efficient process to produce hydrogen. It is liable to produce hydrogen at low cost, thanks to an electrical consumption as low as 3.5 kWh per Nm3of hydrogen produced, considering a heat source available to produce steam around 150°C. A smart heat management into the SOE system needs to be found to heat this steam at 150°C up to the operating temperature of the SOE which is in the range of 700-800°C. Therefore it appears that the steam conversion rate is a key parameter in the overall efficiency of the system. Most generally, due to concentration overpotentials leading to a limiting current at the cell level, to which a non-homogeneous distribution of gases into the stack can be added, steam conversion rates of 50% or below are reported. Then large flows of steam need to be overheated up to the operating temperature, which impacts the overall efficiency of the system. The present paper presents the methodology undertaken to develop a stack design able to operate at high steam conversion rates (80% or above). Starting from a reference stack design made of 25 cathode supported cells and ferritic stainless steel interconnects, modifications of the design have been made to improve the gas distribution to the cells and its homogeneity in the stack. This improvement has first been validated at the scale of a SRU (single repeat unit, the cell active area being equal to 100 cm²). I-V curves have been recorded at 800°C, with a cathodic inlet flow constituted of 90%H2O/10%H2and an anodic flow made of air, the total cathodic flow rate varying between 9 and 30 NmL/min/cm². It has been shown that with these design modifications, i-V curves can be recorded up to a steam conversion rate of 100%. For an inlet flow rate very low, equal to 9 Nml/min/cm², the 100% of steam conversion rate is reached for a current density of 1.15 A/cm² at 1.23V (figure 1). For an inlet flow rate of 12 Nml/min/cm², the 100% of steam conversion rate is reached for a higher current density of 1.56 A/cm² at 1.40V. In both cases, a limiting current appeared only far on the curve, for a current density of 1.06 A/cm² in the first case and 1.45 A/cm² in the second case. I-V curves have been plotted with several other inlet flow rates, and it has been shown that the lower is the inlet flow rate, the higher is the maximum steam conversion rate. However, even for the highest flow rates tested (24 and 30 NmL/min/cm²), the maximum reachable steam conversion rates remain respectively at values of 90 and 80%. These results validate the design modification, which was then implemented at the scale of a 25-cell stack. The reference stack design, prior any modification, reaches, at 800°C, a current density of 0.85 A/cm² at 1.2V in average per cell, for a steam conversion rate of 55% and a corresponding inlet flow rate of 12 NmL/min/cm². For this value of steam conversion rate, a limiting current starts to appear. On the contrary, with the design modification, no limiting current appeared on the i-V curve. For the highest current density recorded, equal to 1.0 A/cm², corresponding to an voltage of 1.17 V/cell, the steam conversion rate was of 64% with a corresponding inlet flow rate of 12 NmL/min/cm². Therefore it can be concluded that thanks to the design modifications, the limiting current is shifted to values well above 65%, and that the stack can be operated at high steam conversion rate. Several 25-cell stacks have been produced with either the reference design or the modified design. With the reference stacks, the voltage scattering among the 25 cells of the stack is around 15%, which is acceptable. Thanks to the design modification, the scattering decreases to a value around 5%. It shows that the design modification not only allows to reach higher steam conversion rates, but also improves the gas distribution homogeneity into the stack. Finally, a 25-cell stack with the modified design has been integrated into a SOE system including all BoP components. It has been possible to operate this stack, at the system level, up to a value of steam conversion of 80%. Figure 1

Opportunity Cost Quantification for Ancillary Services Provided by Heating, Ventilating, and Air-Conditioning Systems

In ancillary service electricity markets, opportunity costs account for the profit forgone by a generator due to the use of generating capacity for ancillary service provision instead of energy sales. They are calculated for each generator providing ancillary services and are included in the determination of market-clearing prices by market operators throughout the U.S. Recent interest in the use of smart heating, ventilating, and air-conditioning systems for ancillary service provision prompts an investigation into their associated opportunity costs. This paper proposes a definition for these costs and method of accounting for them through time. This is done by recognizing the impacts intra-hour consumption modification associated with ancillary service provision have on daily energy efficiency and costs. A numeric example is presented to demonstrate the method. It is found that marginal ancillary service opportunity costs vary over time and that properly adjusting energy consumption at an hour of interest can reduce the total opportunity cost of providing ancillary services.

6-4-2 特定既成市街地における地域冷暖房事業者と連携したスマートヒートグリッドについて : SHG構想(第2報)(Session 6-4 エネルギーネットワーク)

6-4-2 特定既成市街地における地域冷暖房事業者と連携したスマートヒートグリッドについて : SHG構想(第2報)(Session 6-4 エネルギーネットワーク)

6-2-1 スマートヒートグリッド構想について(第1報)(Session 6-2 エネルギーマネージメント)

6-2-1 スマートヒートグリッド構想について(第1報)(Session 6-2 エネルギーマネージメント)

6-1-2 既存地域冷暖房に適用したスマートヒートグリッドの省エネルギー性及び適応可能性に関する研究（第3 報）

6-1-2 既存地域冷暖房に適用したスマートヒートグリッドの省エネルギー性及び適応可能性に関する研究（第3 報）