Temperature Set Point Research Articles

A Comparison of Classical and Reinforcement Learning-based Tuning Techniques for PI controllers

This study compares two tuning techniques for Proportional-Integral (PI) controllers. The first strategy uses the Pole-Zero Cancellation method, a well-established technique in the field of dynamic systems control. The second strategy introduces an innovative approach by using a reinforcement learning-based technique to adaptively tune a PI controller. To compare these tuning methodologies, a Heating, Ventilation and Air Conditioning (HVAC) control system was selected as a case study to guarantee users’ thermal comfort. In both cases, the HVAC process was modelled as a first-order system without time delay. In addition, the performance of the proposed controllers analysed by evaluating temperature set-point tracking and disturbance rejection caused by the occupancy of people in the room. The results demonstrate that classical methods are efficient and quick to implement, while the use of RL also enables optimization of energy consumption and reduction of operating costs.

Expanding Building Archetypes to Estimate the Indoor Environment Thermal Storage Capacity in the Danish Building Stock when Performing Demand Response

Short-term demand response leveraging the energy flexibility of the building stock is a key solution to decarbonise and ensure the reliability and sustainability of smart energy grids dominated by intermittent renewable energy sources. Most of the building demand response capacity depends on different forms of energy storage in the built environment. In addition to electric batteries, hot water storage tanks, ground source heat exchanger or dispatchable on-site renewable energy sources, buildings possess a tremendous thermal storage capacity within the thermal inertia of their structure and indoor environment. The latter can easily be harnessed with heating/cooling indoor temperature setpoint modulation strategies. However, there is a clear lack of large-scale estimates of this indoor environment thermal storage capacity in the different building stocks. To tackle this issue, detailed dynamic numerical models of the different building typologies in European countries are generated to assess the potential of demand response strategies at nationwide levels. The current paper presents the preliminary results on the thermal storage capacity in the Danish building stock when performing heating temperature setpoint modulation. These results are based on estimates of the effective thermal inertia of the different building archetypes in Denmark. They show that the Danish building stock contains immense thermal storage capacity, which is comparable to all combined batteries in a large fleet of electric vehicles or all industrial-size storage tanks in district heating plants.

The Sub-Metered HVAC Implemented for Demand Response Dataset

Abstract Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research.

Turn down your thermostats – A contribution to overcoming the European gas crisis? The example of Germany

Europe's current gas crisis requires rapid government intervention to curtail natural gas consumption and mitigate expenses for consumers. This study aims to comprehensively assess the impact of adjusting thermostats, with single-family houses in Germany serving as a case study. A unique bottom-up approach for approximating gas consumption at the level of building archetypes reveals that decreasing temperature setpoints from 21 °C to 19 °C and 17 °C can save about 14 and 30 TWh/a of gas, respectively. This corresponds to 3–6 % of gas imports from Russia to Germany in 2020. The largest absolute savings can be realized in older and larger buildings. Additionally, our findings suggest that the adjustment of thermostats may decrease residential CO2 emissions by 3–6 Mt/a, achieved through a reduction of 2–4 °C in the setpoint. Therefore, the measure shows great promise regardless of the present crisis. From the consumer's perspective, a 1 °C temperature reduction can lead to a gas bill reduction of 4–9 %, contingent upon building type. Nevertheless, the cost burden associated with rising gas prices surpasses these savings. Residents of older buildings suffer more severe financial impacts than those in newer ones. Our research suggests that policymakers should consider implementing adjustments to residential thermostats. Furthermore, consumer financial assistance programs should factor in building type when designing relief mechanisms.

Water Quality Trade-offs for Risk Management Interventions in a Green Building.

Premise plumbing water quality degradation has led to negative health impacts from pathogen outbreaks (e.g., Legionella pneumophila and non-tuberculous mycobacteria), as well as chronic effects from exposure to heavy metals or disinfection by-products (DBP). Common water quality management interventions include flushing, heat shock (thermal disinfection), supplemental disinfection (shock or super chlorination), and water heater temperature setpoint change. In this study, a Legionella pneumophila- colonized Leadership in Energy and Environmental Design (LEED) certified building was monitored to study health-relevant water quality changes before and after three controlled management interventions: (1) flushing at several points throughout the building; (2) changing the water heater set point; and (3) a combination of interventions (1) and (2) by flushing during a period of elevated water heater set point (incompletely performed due to operational issues). Microbial (culturable L. pneumophila, the L. pneumophila mip gene, and cATP) and physico-chemical (pH, temperature, conductivity, disinfectant residual, disinfection by-products (DBPs; total trihalomethanes, TTHM), and heavy metals) water quality were monitored alongside building occupancy as approximated using Wi-Fi logins. Flushing alone resulted in a significant decrease in cATP and L. pneumophila concentrations (p = 0.018 and 0.019, respectively) and a significant increase in chlorine concentrations (p = 0.002) as well as iron and DBP levels (p = 0.002). Copper concentrations increased during the water heater temperature setpoint increase alone to 140°F during December 2022 (p = 0.01). During the flushing and elevated temperature in parts of the building in February 2023, there was a significant increase in chlorine concentrations (p = 0.002) and iron (p = 0.002) but no significant decrease in L. pneumophila concentrations in the drinking water samples (p = 0.27). This study demonstrated the potential impacts of short term or incompletely implemented interventions which in this case were not sufficient to holistically improve water quality. As implementing interventions is logistically- and time-intensive, more effective and holistic approaches are needed for informing preventative and corrective actions that are beneficial for multiple water quality and sustainability goals.

Time stability of the dynamic behaviour of a 3D-printed airless wheel by stereolithography

In this paper, the dynamic behavior of an airless wheel made by additive manufacturing is investigated. A lattice geometry is chosen as the cyclic pattern from which the wheel is built. Initially, a linear numerical model is used to preliminarily assess resonance frequency and mode shapes of the wheel. Afterwards, specimens were tested twice, in August 2020 and after six months, highlighting a relevant frequency and damping shift in the vibrational response. To better understand the reason behind the change over time in the wheel dynamic behavior, dedicated structural dynamics tests at varying temperature and humidity set points were performed in an environmental chamber, consisting in measuring the wheel vibrational response when subjected to temperature and humidity variation.

Parametric analysis of chiller plant energy consumption in a tropical climate

The approach that could reduce the energy consumption of water-cooled chillers in office buildings was investigated through experimental and simulation methods. The chiller was modelled in TRNSYS and validated using physical measurements from an operational water-cooled water chiller and fan coil system. The validated model was used to analyse the energy consumption of the water chiller-fan coil system under a high chilled water temperature setpoint (CWTS) in a representative commercial office building. Subsequently, indoor thermal comfort was evaluated using the PMV-PPD model. Finally, the design of experiment (DOE) was employed using a statistical two-level non-randomized factorial design in Minitab to study the effects of high CWTS, number of rows, and tube diameter on the heat and mass transfer performance of the fan coil unit. The results showed that the CWTS can be increased by various degrees from 10 up to 18 °C for energy efficiency for a commercial office building in the tropics. This increase in CWTS would result in a daily energy saving potential of about 5% of the chiller as compared to the existing operational settings without any extra cost. Conversely, the daily energy consumption by the fan coil would increase by about 5.5% by this increment in the CWTS. It was determined that the chiller system can provide comfort even when the CWTS is increased to 14 °C. The DOE analysis showed that under the condition of high CWTS (14 °C), energy consumption that is less than the current energy consumption may be expected from the fan coil system when the number of rows increased from 3 to 6 and the tube diameter increased from 7 to 9 mm.

Design and control intended low-order model for transient investigation of an electric oven: Static mode

In general, electric ovens represent a low-efficiency class among home appliances, contributing to overall environmental changes in buildings and apartments. To improve their low efficiency, which is typically between 10% to 12%, industries are increasingly focused on developing effective technologies. One suitable strategy is to design and control the oven's center temperature, as specified by the EN 60350-1 European standard, which regulates the energy efficiency index (EEI) test. The present simulation study explains the transient thermo-fluid behavior of a domestic electric oven subjected to free convection, i.e., static mode. For this purpose, appropriate dynamic models are employed, intended for design and control. A lumped-parameter based methodology is considered to develop a low-order dynamic model, resulting in reduced computational cost and run time. A finite level of discretization is introduced into the model, enabling the capture of limited insights, such as temperature, heat flux, mass flow rate, pressure drop, etc., particularly at the cavity center, internal and external cavity wall surfaces, and at suction and exit openings. The electric oven is mainly divided into four sub-systems: door, cavity, ventilation, and cabinet. Each sub-system has a suitable level of discretization signifying a lumped mass parameter to reproduce the transient behavior within the electric oven. Heaters are considered as individual lumped masses and are integrated within the cavity sub-system. The above sub-systems employ both the first principle equations and correlations based on experimental data, giving a low-order grey-box identity to the system. In addition to conduction and convection, a non-linear form of radiation is considered to increase model accuracy. The developed model is further calibrated with available physical data. Once calibrated, the model is then compared with the remaining physical data produced at different heat loads to understand the accuracy in terms of transient temperature prediction at various temperature set points.

Steady melting in material extrusion additive manufacturing

PurposeA main cause of defects within material extrusion (MatEx) additive manufacturing is the nonisothermal condition in the hot end, which causes inconsistent extrusion and polymer welding. This paper aims to validate a custom hot end design intended to heat the thermoplastic to form a melt prior to the nozzle and to reduce variability in melt temperature. A full 3D temperature verification methodology for hot ends is also presented.Design/methodology/approachInfrared (IR) thermography of steady-state extrusion for varying volumetric flow rates, hot end temperature setpoints and nozzle orifice diameters provides data for model validation. A finite-element model is used to predict the temperature of the extrudate. Model tuning demonstrates the effects of different model assumptions on the simulated melt temperature.FindingsThe experimental results show that the measured temperature and variance are functions of volumetric flow rate, temperature setpoint and the nozzle orifice diameter. Convection to the surrounding air is a primary heat transfer mechanism. The custom hot end brings the melt to its setpoint temperature prior to entering the nozzle.Originality/valueThis work provides a full set of steady-state IR thermography data for various parameter settings. It also provides insight into the performance of a custom hot end designed to improve the robustness of melting in MatEx. Finally, it proposes a strategy for modeling such systems that incorporates the metal components and the air around the system.

Shift work duration and heatstroke risk among healthcare workers during hot summer months: A modelling study across 34 Chinese cities

Heat stress issues in healthcare workers (HCWs) have been widely recognized but no published guidelines have clearly specified healthy shift work hours and the time to experience heatstroke while performing healthcare tasks in outdoor settings. Using a rational biophysical model and hourly meteorological data collected during sixty hot summer months (June–August 2002–2021) in 34 major Chinese cities, this study determined healthy shift work hours and heatstroke risks in HCWs with three levels of body mass index (BMI = 18.0, 23.0, and 27.0 kg/m2) while performing healthcare tasks. Our simulation results found that HCWs should switch shifts every 2 h. HCWs with a higher BMI could see their shifts extended by 10–28 min. HCWs with 18.0 kg/m2 BMI should finish shifts 10–23 min earlier than their counterparts. Heatstroke can strike HCWs in 143 min. HCWs should not wear impermeable PPE suits outdoors for >2 h in cities other than Guiyang, Qingdao, Kunming, Xining, and Lhasa for safety reasons. To ensure 8 h of healthy labor, HCWs are advised to wear personal cooling systems with a cooling power of ≥194.8 W/m2 or to work in testing booths, where HVAC setpoint temperature for PPE-clad HCWs is 25.0 °C, with RH = 50–65 % and a fanning speed of 2.5 m/s.

Peak loads vs. cold showers: the impact of existing and emerging hot water controllers on load management

ABSTRACT Electric Hot Water Cylinders (HWCs) offer considerable Demand Side Management in Aotearoa New Zealand, which can provide load management and increase integration of renewable electricity. In this work, scenario analyses are conducted to simulate the impact on Low Voltage transformer load and demand fulfilment of four HWC controller types: setpoint (the default in Aotearoa New Zealand), ripple, smart-power, and smart-thermostat. All controllers reduce peak electricity demand by 14-34% from setpoint, where 34% is the maximum possible reduction with hot water control. Unmet demand, which indicates insufficient hot water and can lead to negative outcomes such as cold showers, is increased by 120% and 12-69% by ripple and smart-power control, respectively, and decreased by 7-31% by smart-thermostat control. Average thermal losses are 2.25 kWh/day for the setpoint controller, and between 2.20-2.76 kWh/day for other controllers. Smart-power controllers demonstrate demand deferral, shifting peak electricity loads to shoulder loads, while smart-thermostat controllers demonstrate demand deferral and valley filling, shifting peak loads to times of lowest demand and smoothing load distribution. Overall, smart controllers improve load management performance with little-to-no increase in unmet demand or thermal losses. Thus, smart controllers are a viable option for Demand Side Management in Aotearoa New Zealand. Abbreviations and Nomenclature: DHW: Domestic Hot Water; DLC: Dynamic Load Control; DSM: Demand Side Management; EV: Electric Vehicle; GHG: Green House Gas; HV: High Voltage; HWC: Hot Water Cylinder; LDC: Load Duration Curve; LV: Low Voltage; MV: Medium Voltage; NZD: New Zealand Dollar; PV: PhotoVoltaic; TOU: Time Of Use; UD: Unmet Demand; WTP: Willingness To Pay; A: WTP function coefficient; B: WTP function coefficient; Cp: specific heat of water [J/kg/K]; Ctrans: cost imposed by the transformer; HWsuff: ratio of hot water sufficiency; Kloss,h: thermal losses for cylinder h [W/K]; Kmix: thermostatic mixing valve factor; m: WTP function coefficient; Ptotal: transformer power demand [W]; Pcap: transformer capacity [W]; Ph: Household power demand [W].; PHWC: heater element power [W]; Pop: Transformer limit for Type3 controller [W]; QDHW: heat loss from DHW use [W]; Qloss: heat loss from standing losses [W]; th: time horizon [s]; Tamb: ambient temperature [K]; THWC: temperature of the HWC [K]; Tin: water inlet temperature [K]; Tmin: minimum temperature before fulfilment failure; Tout: water outlet temperature [K].; Tset: temperature setpoint of the HWC controller [K]; V ˙ : flow rate of hot water from the HWC [L/s]; Vavail: available DHW in the HWC [L]; VDHW: volume of hot water draw [L]; VDHW,expected: expected time weighted demand; VDHW,expected,max: maximum expected DHW demand; VHWC: volume of the HWC [L]; wf: time weighting function; ρ: density of water [kg/m3].

ComfortGPT: a transformer-based architecture for predicting preferred temperature setpoints leveraging big data

The transformation of personal comfort models into a scalable solution has been hindered by their heavy reliance on occupant feedback across a wide range of environmental conditions, and their inability to adapt to changes in preferences. However, these challenges could be addressed by leveraging large-scale databases to build pre-trained models and using limited occupant interactions to select the most representative model, while simultaneously detecting potential changes in preferences. This paper introduces a novel transformer-based architecture (ComfortGPT), which employs pre-trained models built on the data derived from ECOBEE Donate Your Data program through a uniquely crafted cleaning process. The pre-trained models are derived through clustering the supervised learning models applied to each user's data, thereby capturing the diverse range of thermostat preferences. Due to their generative nature, these models can predict setpoints across a wide range of environmental conditions. In order to adapt to variations in occupant preferences, we adopted a transformer mechanism to encode user thermostat interactions based on time sequence and relevance. We applied an exhaustive search to identify the optimal hyperparameters and achieved R2 of 0.77 and Mean Absolute Error (MAE) of 0.65 °C. We also found that the pre-trained models represent a spectrum of setpoint/outdoor temperature relationships ranging from warm-likers to cool-likers and referred to them as comfort archetypes. These archetypes could be employed in energy simulations and design applications, facilitating more personalized and energy-efficient indoor environments. To support further research and practical applications, we provide a code repository and an interactive graphical interface at https://github.com/Building-Robotics-Lab/ComfortGPT.

Potential for Electricity Reduction in Demand Response Using Building Thermal Storage

Demand response (DR) contributes to filling the gap between electricity supply and demand by controlling energy resources on the consumer side and promoting the installation and use of renewable energy. This study is performed to identify the potential of building thermal storage for power reduction in DR. In addition, the optimal conditions are identified, in particular the on/off status of air-conditioning and changes in room temperature setpoints. A simulation model for an office building equipped with packaged air-conditioners is developed, and dynamic programming is applied to calculate the on/off and load ratios to minimize the cumulative electricity during the DR period. Based on the results, two types of operations are suggested: one is to maintain the temperature at the upper allowable limit (in cooling) during the DR period, and the other is to repeatedly turn the air-conditioner on and off. In addition, the following factors affect power reduction: the allowable room temperature range, the time between the request for reduction and the start of the DR period, and the partial load characteristics of the air-conditioning equipment.

Quantifying Energy Savings from Optimal Selection of HVAC Temperature Setpoints and Setbacks across Diverse Occupancy Rates and Patterns

With the advent of flexible working arrangements, we are observing a dramatic shift in how buildings are occupied today, which presents an opportunity to optimize Heating, Ventilation, and Air Conditioning system temperature setpoints based on variations in occupancy. Guidelines often suggest the adoption of the highest or lowest setpoint or setback to minimize energy consumption in hot or cold climates, respectively. However, at outdoor temperatures where variations in occupancy heat loads prompt buildings to fluctuate across cooling, free-running, and heating mode, optimal setpoints and setbacks are not always the lowest or highest. In addition, the perturbations caused by rapid switching between setpoint and setback could diminish energy savings due to system destabilization. This paper aims to systematically compare the potential energy savings from fixed and optimal setpoints and setbacks across wide-ranging occupancy scenarios (four occupancy rates and 14 patterns). Energy simulations were conducted using the Department of Energy reference models for small, medium, and large office buildings to enable an exhaustive search of optimal setpoint/setbacks in 17 climate zones. Explored setpoints were 19.5 °C to 25.5 °C with intervals of 1 °C, and setbacks were 17 °C/19 °C for heating and 26 °C/28 °C for cooling. The findings indicate that, on average, while lower occupancy heat loads results in 5.48% energy reduction, a conventional fixed setpoint and setback strategy provides an additional 11.80%, and optimal selection of setpoints and setbacks could provide an additional 34.36–38.08%, emphasizing the untapped potential energy saving. To facilitate practical applications, this paper presents an interactive graphical interface: Optimal Temperature Setpoint Tool.

Two-stage robust scheduling and real-time load control of community microgrid with multiple uncertainties

With the increasing popularity of wind and solar generation, the stable and economic operation of the community microgrid system become more challenging. This paper is concerned with the robust scheduling and real-time control of demand response (DR) air conditioning (AC) loads for community microgrid. Firstly, a two-stage robust scheduling model coupled by the day-ahead dispatch and real-time dispatch is established by considering the uncertainties of wind/photovoltaic power generation and loads. Moreover, the incorporation of multiple interval uncertain sets is introduced to characterize the uncertainties related to wind power, photovoltaics, and load, thereby reducing the conservativeness of the model. Secondly, the real-time control of AC loads by adjusting the on/off actions and temperature setpoints with limited impact on user comfort so as to track the optimized electricity consumption curve. Finally, the simulation results show the effectiveness and robustness of the proposed optimization dispatch and real-time control algorithm.

Bidding strategy of the virtual power plant consisting of thermal loads controlled by thermostats for providing primary frequency control ancillary service

The purpose of this article is to provide a model for the effective use of on/off loads and thermal loads controlled by thermostats (TCL) in providing primary frequency control (PFC). To this end, the mentioned loads have been aggregated in the form of a virtual power plant (VPP), allowing their cumulative capability to be offered as a bid for ancillary services. In order to determine VPP bids, a droop curve similar to the droop curve of traditional power plants has been extracted for VPP, which includes the technical limitations of VPP member loads. In formulating the VPP bidding strategy, the goal is to maximize profits and to use the capabilities of TCLs more effectively; their temperature set point is considered variable, as well as the constraint of compressor lockout, energy consumption level, device’s fire frequency and the duration of their continued response (deployment end) are incorporated in the model. The results of the model are the maximum power that can be provided to the primary frequency ancillary service market and its profit. The presented model has been simulated on a test system including 70 domestic loads in MATLAB software and checked under five different scenarios. According to the obtained results, considering the variable temperature set point for TCL leads to increasing the power of VPP by 7.33%. Moreover, considering compressor lockout constraint and increasing deployment end lead to a 22.4% and 3.54% reduction to the power of VPP respectively.

STEAM Experimental Facility: A Step Forward for the Development of the EU DEMO BoP Water Coolant Technology

Within the EUROfusion roadmap for the technological development of the European-DEMOnstration (EU-DEMO) reactor, a key point has been identified in the discontinuous operation (pulse-dwell-pulse) of the machine. Water Cooled Lithium Lead (WCLL) Breeding Blanket (BB) Primary Heat Transfer Systems (PHTSs) adopt technology and components commonly used in nuclear fission power plants, whose performances could be negatively affected by the above mentioned pulsation, as well as by low-load operation in the dwell phase. This makes mandatory a full assessment of the functional feasibility of such components through accurate design and validation. For this purpose, ENEA Experimental Engineering Division at Brasimone R.C. aims at realizing STEAM, a water operated facility forming part of the multipurpose experimental infrastructure Water cooled lithium lead -thermal-HYDRAulic (W-HYDRA), conceived to investigate the water technologies applied to the DEMO BB and Balance of Plant systems and components. The experimental validation has the two main objectives of reproducing the DEMO operational phases by means of steady-state and transient tests, as well as performing dedicated tests on the steam generator aiming at demonstrating its ability to perform as intended during the power phases of the machine. STEAM is mainly composed of primary and secondary water systems reproducing the thermodynamic conditions of the DEMO WCLL BB PHTS and power conversion system, respectively. The significance of the STEAM facility resides in its capacity to amass experimental data relevant for the advancement of fusion-related technologies. This capability is attributable to the comprehensive array of instruments with which the facility will be equipped and whose strategic location is described in this work. The operational phases of the STEAM facility at different power levels are presented, according to the requirements of the experiments. Furthermore, a preliminary analysis for the definition of the control strategy for the OTSG mock-up was performed. In particular, two different control strategies were identified and tested, both keeping the primary mass flow constant and regulating the feedwater mass flow to follow a temperature set-point in the primary loop. The obtained numerical results yielded preliminary feedback on the regulation capability of the DEMO steam generator mock-up during pulsed operation, showing that no relevant overtemperature jeopardized the facility integrity, thanks to the high system responsivity to rapid load variations.

Investigation on the thermal control and performance of PCM–porous media-integrated heat sink systems: Deep neural network modelling employing experimental correlations

Phase change material (PCM)-based heat sinks can offer reliable and effective thermal management (TM) solutions for increasingly sophisticated applications. A critical aspect of such heat sinks is determining how long it takes them to reach a set-point temperature. However, no generalised method exists in the literature that can predict and interpret the thermal performance of a wide range of PCM–porous media-integrated heat sinks. In this regard, this study examines the heat transfer characteristics of PCM-based heat sinks integrated with various metallic foams through experimental and deep learning (DL) techniques. The experiments are performed for transient TM analysis of various PCM-based heat sinks. Diverse variables, including foam porosity (0.95–0.97), PCM fraction (0.6–0.8), heat flux (0.8–2.4 kW/m2), foam materials (Fe–Ni alloy, Ni and copper) and PCM type (RT-35HC, RT-44HC, RT-54HC and paraffin wax), are investigated in this study. The experimental data are fed to the optimal DL model using the Bayesian surrogate model-tuned hyperparameters. Utilising a correlation analysis, as exemplified by the heat map and correlation plot, in conjunction with explainable artificial intelligence, it has been deduced that the thermal performance of the heat sink is principally influenced by factors such as PCM type, PCM fraction, foam material, foam porosity, and heat flux. Comparing the model's predicted data with the empirical findings, a good agreement was observed. Specifically, the mean absolute error (MAE) for the anticipated temperature and gradient registered at 0.0438 and 0.0054, whilst the mean square error (MSE) manifested values of 0.0579 and 0.0087, respectively. The proposed model can accurately assess the heat sink's thermal performance (correlation coefficient, R2 = 0.99) for various PCM types, fractions, foam materials, applied heat flux and foam porosity.

Assessing the energy saving potential of using adaptive setpoint temperatures: The case study of a regional adaptive comfort model for Brazil in both the present and the future

Assessing the energy saving potential of using adaptive setpoint temperatures: The case study of a regional adaptive comfort model for Brazil in both the present and the future

Techno-economic feasibility assessment of a photovoltaic water heating storage system for self-consumption improvement purposes

Photovoltaic excess energy export to the grid starts to be a common issue among residential grid-tied PV systems. Its impact is more significant when the feed-in tariffs are not available (Moroccan case) or if they are far from competition. Load shifting, batteries and thermal energy storage all have the potential to diminish the excess energy sent to the grid. However, the water heating strategy has received little literature attention so far. In this study, we seek to assess the techno-economic feasibility of using the PV excess energy in a hot water storage tank by means of a diverter as the main water heating system for a bioclimatic building. The excess energy of a 2kWp grid-tied PV system has been monitored for one year with a 1-min time resolution. The data is used within a TRNSYS numerical simulation for the city of Marrakesh, Morocco. In comparison to an ordinary solar thermal water heating (STWH) system, the results of the simulation revealed that both systems maintain the setpoint temperature using practically the same amount of auxiliary energy. Additionally, a 52.23 % relative improvement to the self-consumption rate has been obtained by integrating a PV water heater (PVWH). The Levelized Cost of Energy found to be 33 % lower for the PVWH compared to that of the STWH solution, evaluated at 0.06 and 0.09 $/kWh respectively. In addition, PVWH's Payback Time period was four years, while that of the STWH was six years. Ultimately, the Net Present Value was significantly higher for the PVWH (1840.28 $) than for the STWH (1008.8 $).