Search Results (64)

The significant contribution of buildings to the global primary energy consumption necessitates the application of energy management methodologies at a building scale. Although dynamic simulation tools and decision-making algorithms are core components of energy management methodologies, they are often accompanied by excessive computational cost. As future controlling structures tend to become autonomized in building heating layouts, encouraging distributed heating services, the research scope calls for creating lightweight building energy system modeling as well monitoring and controlling methods. Following this notion, the proposed methodology turns a programmable controller into a smart thermostat that utilizes surrogate modeling formed by the ALAMO approach and is applied in a 4-m-by-4-m-by-2.85-m environmental chamber setup heated by a heat pump. The results indicate that the smart thermostat trained on the indoor environmental conditions of the chamber for a one-week period attained a predictive RMSE of 0.082–0.116 °C. Consequently, it preplans the heating hours and applies preheating controlling strategies in real time effectively, using only the computational power of a conventional controller, essentially managing to attain at least 97% thermal comfort on the test days. Finally, the methodology has the potential to meet the requirements of future building energy systems featured in urban-scale RES-based district heating networks. Full article

To achieve efficient prediction and optimization of the energy consumption of ship central cooling systems, this paper first constructed and validated a high-precision multi-physical domain simulation model of the ship central cooling system based on fluid heat transfer principles and the physical network method. Then, simulation experiments were designed using the Box–Behnken design (BBD) method to study the effects of five key parameters—main engine power, seawater temperature, seawater pump speed, low-temperature fresh water three-way valve opening, and low-temperature fresh water flow rate—on system energy consumption. Based on the simulation data, an energy consumption prediction model was constructed using response surface methodology (RSM). This prediction model exhibited excellent goodness of fit and prediction ability (coefficient of determination R² = 0.9688, adjusted R²_adj = 0.9438, predicted R²_pred = 0.8752), with a maximum relative error of only 1.2% compared to the simulation data, confirming its high accuracy. Sensitivity analysis based on this prediction model indicated that main engine power, seawater pump speed, seawater temperature, and three-way valve opening were the dominant single factors affecting energy consumption. Further analysis revealed a significant interaction between main engine power and seawater pump speed. This interaction resulted in non-linear changes in system energy consumption, which were particularly prominent under operating conditions such as high power. This study provides an accurate prediction model and theoretical guidance on the influence patterns of key parameters for the simulation-driven design, operational optimization, and energy saving of ship central cooling systems. Full article

With the enhancement of thermodynamic cycle parameters and heat dissipation constraints in aero-engines, effective thermal management has become a critical challenge to ensure safe and stable engine operation. This study developed a transient temperature evaluation model applicable to the entire flight envelope, considering fluid–solid coupling heat transfer on both the main flow path and fuel systems. Firstly, the impact of heat transfer on the acceleration and deceleration performance of a low-bypass-ratio turbofan engine was analyzed. The results indicate that, compared to the conventional adiabatic model, the improved model predicts metal components absorb 4.5% of the total combustor energy during cold-state acceleration, leading to a maximum reduction of 1.42 kN in net thrust and an increase in specific fuel consumption by 1.18 g/(kN·s). Subsequently, a systematic evaluation of engine thermal management performance throughout the complete flight mission was conducted, revealing the limitations of the existing thermal management design and proposing targeted optimization strategies, including employing Cooled Cooling Air technology to improve high-pressure turbine blade cooling efficiency, dynamically adjusting low-pressure turbine bleed air to minimize unnecessary losses, optimizing fuel heat sink utilization for enhanced cooling performance, and replacing mechanical pumps with motor pumps for precise fuel supply control. Full article

Accurately forecasting load profiles, especially peak catching, is a challenge due to the stochastic nature of consumption. In this paper, we applied the following three models for forecasting: Long Short-Term Memory (LSTM); Support Vector Regression (SVR); and the combined model, which is a blend of SVR, Gated Recurrent Units (GRU), and Linear Regression (LR) to forecast 24 h-ahead load profiles. Household (HH), heat pump (HP), and electric vehicle (EV) loads are singular, and these were collectively considered with one-year load profiles. This study tackles the issue of accurately forecasting load profiles by evaluating LSTM, SVR, and an ensemble model for predicting energy consumption in HH, HP, and EV loads. A novel forecast correction mechanism is introduced, adjusting forecasts every eight hours to increase reliability. The findings highlight the potential of deep learning in enhancing energy demand forecasting, especially in identifying peak loads, which contributes to more stable and efficient grid operations. Visual and validation data were investigated, along with the models’ performances at different levels, such as off-peak, on-peak, and entirely. Among all models, LSTM performed slightly better in most of the factors, particularly in peak capturing. However, the blended model showed slightly better performance than LSTM for EV power load forecasting, with an on-peak mean absolute percentage error (MAPE) of 21.45%, compared to 29.24% and 22.02% for SVR and LSTM, respectively. Nevertheless, visual analysis clearly showed the strong ability of LSTM to capture peaks. This LSTM potential was also shown by the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) during the on-peak period, with around 3–5% improvement compared to SVR and the blended model. Finally, LSTM was employed in predicting day-ahead load profiles using measured data from four grids and showed high potential in capturing peaks with MAPE values less than 10% for most of the grids. Full article

Energy consumption in buildings is a major contributor to greenhouse gas emissions, primarily due to the extensive burning of fossil fuels. This study focuses on an innovatively designed building named The Clover and utilises IES-VE software (2024) to create a digital twin for the building’s performance prediction. The goal is to achieve a zero-carbon-emission building through energy-efficient strategies, including the use of air-source heat pumps and renewable energy systems for sustainable heating, cooling, and electricity. Dynamic simulations conducted with the software analyse key performance metrics, including annual heating and cooling demands, electricity consumption, carbon emissions, and renewable energy supply. The results indicate that a 53% reduction in CO₂ emission is achieved when a heat pump system is applied instead of boiler and chiller systems. A total of 1243.96 MWh and 41.18 MWh of electricity can be generated by PV panels and wind energy systems. The net annual electricity generation from the energy system of the building is 191.64 MWh. Therefore, the results demonstrate that the building’s energy needs can be successfully met through on-site electricity generation using advanced perovskite–silicon tandem solar PV panels and wind turbines. This case study provides valuable insights for architects and building services engineers, offering a practical framework for designing green, energy-efficient, zero-carbon buildings and advancing the path to net zero. Full article

EU regulations get stricter from 2028 on by imposing net-zero energy building (NZEB) standards on new residential buildings including on-site renewable energy integration. Heat pumps (HP) using thermal building mass, and Model Predictive Control (MPC) provide a viable solution to this problem. However, the MPC potential in NZEBs considering the impact on indoor comfort have not yet been investigated comprehensively. Therefore, we present a co-simulative approach combining MPC optimization and IDA ICE building simulation. The demand response (DR) potential of a ground-source HP and the long-term indoor comfort in an NZEB located in Vorarlberg, Austria over a one year period are investigated. Optimization is performed using Mixed-Integer Linear Programming (MILP) based on a simplified RC model. The HP in the building simulation is controlled by power signals obtained from the optimization. The investigation shows reductions in electricity costs of up to 49% for the HP and up to 5% for the building, as well as increases in PV self-consumption and the self-sufficiency ratio by up to 4% pt., respectively, in two distinct optimization scenarios. Consequently, the grid consumption decreased by up to 5%. Moreover, compared to the reference PI controller, the MPC scenarios enhanced indoor comfort by reducing room temperature fluctuations and lowering the average percentage of people dissatisfied by 1% pt., resulting in more stable indoor conditions. Especially precooling strategies mitigated overheating risks in summer and ensured indoor comfort according to EN 16798-1 class II standards. Full article

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications. Full article

Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual learning algorithms to personalize DHW predictions of household occupants’ behavior. Such models, alongside a control system that decides when to heat, enable the development of a heat-pumped-based smart DHW production system, which can heat water only when needed and avoid energy loss due to the storage of hot water. Deep learning models, and attention-based models particularly, can be used to predict time series efficiently. However, they suffer from catastrophic forgetting, meaning that when they dynamically learn new patterns, older ones tend to be quickly forgotten. In this work, the continuous learning of DHW consumption prediction has been addressed by benchmarking proven continual learning methods on both real dwelling and synthetic DHW consumption data. Task-per-task analysis reveals, among the data from real dwellings that do not present explicit distribution changes, a gain compared to the non-evolutive model. Our experiment with synthetic data confirms that continual learning methods improve prediction performance. Full article

Due to the hot climate, energy consumption for refrigeration is significantly higher in the subtropical monsoon climate region. Combined with renewable energy and ice-storage technology, a model predictive control model of the regional cooling system was proposed, which was conducive to improving the flexibility of the regional cooling system and the ability of peak shifting and valley filling. In this model, an artificial bee colony (ABC) optimized back propagation (BP) neural network was used to predict the cooling load of the regional cooling system, and the model parameter identification method was adopted, combining utilizing a river-water-source heat pump and ice-storage technology. The results showed that the load prediction algorithm of the ABC-BP neural network had a high accuracy, and the variance coefficient of load prediction root-mean-square error (RMSE) was 16.67%, which was lower than BP, support vector regression (SVR), and long short-term memory (LSTM). In addition, compared with the three control strategies of chiller priority, ice-storage priority, and fixed proportion, the operation strategy optimized by the comprehensive model can reduce the average daily cost by 19.20%, 4.45%, and 5.10%, respectively, and the maximum daily energy consumption by 30.02%, 18.08%, and 8.90%, respectively. Full article

Further advances in hardware and software features are needed to optimize battery and thermal management systems to allow for the execution of longer trips in electric vehicles. This paper assesses the economic and environmental impacts of the following features: eco-charging, eco-driving, smart fast charging, predictive thermal powertrain and cabin conditioning, and an advanced heat pump system. A Total Cost of Ownership (TCO) and externalities calculation is carried out on two passenger cars and one light commercial vehicle (LCV). The energy consumption data from the vehicles are based on experiments. The analysis shows more benefits for the LCV, while the smart fast-charging feature on the car shows a slight increase in TCO. However, negative results did not contribute significantly compared to the ability to install a smaller battery capacity for similar use. Full article

Air compressors are widely used in industrial fields. Compressed air systems aggregate air flows and then supply them to places of demand. These huge systems consume a significant amount of energy and generate heat internally. Machine components in compressed air systems are vulnerable to heat, and, in particular, a radiator to cool the heat of the overall air compressor is the core component. Dirty radiators increase energy consumption due to anomalous cooling. To reduce the energy consumption of air compressors, this mechanism emphasizes a machine learning-based radiator fault detection, using features such as RPM, motor power, outlet pressure, air flow, water pump power, and outlet temperature with slight true fault labels. Moreover, the proposed system adds an LSTM-based motor power prediction model to point out the initial judgment of radiator fault possibility. Via the rigorous analysis and the comparison among machine learning models, this meticulous approach improves the performance of radiator fault prediction up to 93.0%, and decreases the mean power consumption of the air compressor around 2.24%. Full article

The global population’s growth and increased energy consumption have driven greenhouse gas (GHG) emissions. In Canada, the residential sector accounts for 17% of secondary energy use and 13% of GHG emissions. To mitigate GHG emissions, promoting renewable energy and efficient heating systems is crucial, especially in cold climates like Canada, where there is a heavy dependency on fossil fuels for space heating applications. A viable solution is hybrid fuel heating systems that combine electric-driven air-source heat pumps (ASHPs) with natural gas tankless water heaters (TWHs). This system can alternate its operation between the ASHP and TWH based on efficiency and real-time energy costs, reducing grid peak demand and enhancing resilience during power outages. Although lab experiments have shown its benefits, in situ performance lacks evaluation. This study analyzes the in situ energy performance of a net-zero ready house and its hybrid fuel heating system, assessing energy consumption, hourly space heating output, and system heating performance. HOT2000 is a robust simulation software designed for assessing energy consumption, space heating, cooling, and domestic hot water systems in residential buildings. An artificial neural network model was developed to predict the energy performance of the hybrid fuel system, which was used as a substitute for monitored data for evaluating the HOT2000’s simulation results under the same weather conditions. Therefore, this study proposes a comprehensive framework for the in situ performance analysis of hybrid fuel heating systems. This study then, using HOT2000 energy consumption results, evaluates the life cycle costs of the hybrid fuel system against conventional heating systems. Furthermore, this study proposes an economical control strategy using in situ data or manufacturer specifications. Full article

The electric vehicle thermal management system (EVTMS) plays a crucial role in ensuring battery efficiency, driving range, and passenger comfort. However, EVTMSs still face unresolved challenges, such as accurate modeling, compensating for temperature variations, and achieving efficient control strategies. Addressing these issues is crucial for enhancing the performance, reliability, and energy efficiency of electric vehicles. Therefore, this study presents a cooling EVTMS model, considering both the battery pack temperature and the cabin comfort, and utilizes the prediction of neural network as a feedforward in a fuzzy PI controller to compensate for the model temperature variations. The simulation results reveal that, compared with PI controller and MPC, the neural network fuzzy PI (NN-Fuzzy PI) controller can well predict and compensate for the system’s nonlinear characteristics as well as the time-delay caused by heat transfer, achieving superior control performance and reducing energy consumption. The battery pack temperature and $\mathrm{PMV}$ fluctuations are effectively constrained within [−0.5, 0.5] and [−0.1, 0.1], reducing up to 150% and 164%, and the energy consumption of the pump and compressor are reduced by up to 0.23 and 100.1 $\mathrm{KJ}$, with ranges of 18% and 2.68%. Meanwhile, the neural network feedforward also works effectively in different controllers. The findings of this research can provide valuable insights for TMS engineers to select advanced control strategies. Full article

Accessing the demand-side management potential of the residential heating sector requires sophisticated control capable of predicting buildings’ response to changes in heating and cooling power, e.g., model-predictive control. However, while studies exploring its impacts both for individual buildings as well as energy markets exist, building-level control in large-scale energy system models has not been properly examined. In this work, we demonstrate the feasibility of the open-source energy system modelling framework Backbone for simplified model-predictive control of buildings, helping address the above-mentioned research gap. Hourly rolling horizon optimisations were performed to minimise the costs of flexible heating and cooling electricity consumption for a modern Finnish detached house and an apartment block with ground-to-water heat pump systems for the years 2015–2022. Compared to a baseline using a constant electricity price signal, optimisation with hourly spot electricity market prices resulted in 3.1–17.5% yearly cost savings depending on the simulated year, agreeing with comparable literature. Furthermore, the length of the optimisation horizon was not found to have a significant impact on the results beyond 36 h. Overall, the simplified model-predictive control was observed to behave rationally, lending credence to the integration of simplified building models within large-scale energy system modelling frameworks. Full article

This work presents the results of a study that used a model based on rough set theory (RST) to assess the brine temperature of vertical ground heat exchangers (VGHEs) to feed heat pumps (HP). The purpose of this research was to replace costly brine temperature measurements with a more efficient approach. The object of this study was a public utility building located in Poland in a temperate continental climate. The building is equipped with a heating system using a brine–water HP installation with a total capacity of 234.4 kW, where the lower heat source consists of 52 vertical ground probes with a total length of 5200 m. The research was conducted during the heating season of 2018/2019. Based on the data, the heat energy production was determined, and the efficiency of the system was assessed. To predict the brine temperature from the lower heat source, a model based on RST was applied, which allows for the analysis of general, uncertain, and imprecise data. Weather data, such as air temperature, solar radiation intensity, degree days of the heating season, and thermal energy consumption in the building, were used for the analysis. The constructed model was tested on a test dataset. This model achieved good results with a Mean Absolute Percentage Error (MAPE) of 12.2%, a Coefficient of Variation Root Mean Square Error (CV RMSE) of 14.76%, a Mean Bias Error (MBE) of −1.3%, and an R-squared (R²) value of 0.98, indicating its usefulness in estimating brine temperature. These studies suggest that the described method can be useful in other buildings with HP systems and may contribute to improving the efficiency and safety of these systems. Full article