Heating Energy Consumption Of Buildings Research Articles

Predicting daily heating energy consumption in residential buildings through integration of random forest model and meta-heuristic algorithms

This research utilizes a sophisticated hybrid model integrating the Random Forest algorithm with meta-heuristic optimization techniques to estimate heating energy consumption in residential buildings. The study addresses key variables including architectural characteristics, occupancy, and ambient temperature. The primary objective is to enhance the prediction accuracy of heating energy consumption using a novel approach combining Random Forest with various meta-heuristic algorithms. The study employs six combinations of the Random Forest algorithm and meta-heuristic optimizers. To mitigate overfitting, K-Fold cross-validation is implemented during model training. The model's performance is evaluated using five statistical indices: coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), and Theil Inequality Coefficient (TIC). Results demonstrate the hybrid model's high predictive accuracy, with the Arithmetic Optimization Algorithm enhancing Random Forest's performance significantly. Notable statistical achievements include R2 = 0.977201, RMSE = 0.1179, MAE = 0.0573, RAE = 0.0930, and TIC = 0.0187. Additionally, the Ant Lion Optimizer shows excellent convergence, achieving a TIC value of 0.014986 after 101 iterations. The proposed hybrid model significantly outperforms traditional methods in predicting residential heating energy consumption. The integration of Random Forest with advanced meta-heuristic algorithms offers a robust framework for enhancing prediction accuracy in energy consumption modeling.

Optimization Analysis of an Energy-Saving Renovation Scheme for Building Envelopes of Existing Rural Houses Based on a Comprehensive Benefit Evaluation

In the cold regions of China, the existing rural houses are widely distributed and in large numbers. There are widespread problems such as low thermal performance of building envelopes, high building energy consumption, and poor indoor thermal environments. Reducing the energy consumption of building heating by reforming the envelope structure can reduce the environmental pollution caused by heating. In this paper, the existing rural houses in Tongchuan City, Shaanxi Province are taken as the research object, and EnergyPlus software is used to calculate building heating energy consumption, and the schemes are compared and selected via the entropy value method. Based on a comprehensive benefit evaluation, the best scheme for the renovation of building envelopes of rural houses in Tongchuan City is put forward. The research results show that the energy saving rate of buildings can reach more than 50% after renovation. In the evaluation of energy saving, incremental cost, return on investment, carbon emission reduction and unguaranteed hours, the weights are 0.1915, 0.2104, 0.2312, 1755, and 0.187, respectively. The best renovation scheme for rural housing is as follows: the thickness of the XPS board is 90 mm for exterior wall insulation; the thickness of the XPS board is 80 mm for roof insulation; the window-to-wall ratio of additional sunspace is 0.6; and the type of exterior windows is a broken-bridge hollow aluminum window of 6 + 12A + 6 (mm).

Effect of different generated weather climate data file on building energy consumption in different climates of Iran

Accurate estimation of building energy consumption is critical, and it can usually be calculated using simulation software like Energy Plus. The input weather data for software is typically a typical or reference year as a standard weather file, such as TMY2. To best estimate building energy consumption, TMY2 data must be generated intelligently. In the present study, first, several TMY2 data files have been generated from different periods (5–21 years) of available measured weather data (9 separate files). Available measured weather data is for 1998 to 2019. Also, a TMY2 file has been generated from the long-term average for the total period (1998–2019), and two TMY2 files have been generated using the CCWeatherGen software for 2020 and 2050. In the next step, the calculated building energy consumption from those files for an educational building will be compared using Energy Plus software. The primary purpose of this study is to investigate the impact of different generated TMY2 (from different weather data periods) on energy consumption and determine the best period to generate TMY2 files from the available period of 5 to 21 years for the capital of Iran (Tehran), which has a semi-arid climate, and Tabriz, which has a cold and mountainous climate. These cities have different climates. Results show that for Tehran, building heating energy consumption changes by about 10% from other generated TMY2 files (one file generated from 2009 to 2019 and another generated from the 2007 to 2019 period). The result shows that generating TMY2 files from different weather data periods affects building energy consumption. So, a suitable strategy for generating TMY2 files is necessary. As a new strategy, the present study proposes that TMY2 files should be generated based on building cooling and heating energy from recent years. Also, because climate change is an important parameter, a TMY2 file should be generated from recent years’ weather data to accurately predict building energy consumption.

Synthesis of model predictive control based on neural network for energy consumption enhancement in building

The problem addressed in this work is the enhancement of energy efficiency in buildings, primarily focused on heating control. The paper highlights the importance of addressing this challenge, given that HVAC systems are responsible for over 60% of total energy consumption in buildings, making them the largest energy consumers in both the residential and nonresidential sectors. This paper presents a methodology for developing a model predictive control (MPC) system based on artificial neural network (ANN) models, with the aim of improving the energy efficiency of buildings. This approach involves the creation of a training dataset using dynamic thermal simulation tools, enabling the learning of the ANN model that is responsible for predicting future states within an MPC optimization loop. Additionally, the implementation leverages advanced technologies such as Field-Programmable Gate Arrays (FPGAs), specifically the PYNQ board, Wi-Fi modules, and MQTT communication to enhance scalability, deployment, and adaptability. The study evaluates the proposed Neural Network Model Predictive Control (NNMPC) strategy implemented on the PYNQ, reporting a substantial reduction of 38.53% in heating energy consumption in buildings compared to a basic On/Off control approach for 24-hour simulation. This outcome underscores the solutions significant potential for energy savings in building heating systems.

Space heating performance analysis of air source heat pump integrated with image gray recognition-based defrosting controller

In practice, owing to the unreasonable defrosting initiating method of air source heat pumps (ASHPs), mal–defrost occurs frequently, further leading to the increase of building heating energy consumption. In recent years, with the development of image recognition technology in many fields, it has been expected to be used in defrosting initiating control methods of ASHPs. Although former investigations demonstrate that it is feasible and promising, there is still lack of a control strategy to make it become reality. To promote the application of image recognition in defrosting initiating method of ASHPs, a control strategy for the defrosting initiating method based on image gray recognition was proposed in the present work and used in an image gray recognition equipment (IGRE). Then, nine groups of experiments were conducted based on this new IGRE method. Results indicate that the proposed control strategy for the IGRE method is feasible and practical. By employing this new IGRE method, the defrosting accuracy rate and the average COP of the ASHPs are respectively 42.86% and 36.60% higher, compared with those using the conventional T–T method. In complex and variable environments, the defrosting accuracy rate of this new IGRE method reaches 93.33% in a 10–day test.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change.

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool-AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%-31% and 21%-29% for heating energy consumption, an increase of 113%-173% and 95%-144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m2 in the current typical climate to 57 kWh/m2 in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m2 to 32 kWh/m2. The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Factors Influencing Residents to Implement Energy Efficiency Measures in Multi-Apartment Buildings

The review examines in detail the types of factors that may influence residents’ motivation to implement energy efficiency measures in multifamily housing. When analyzing heating energy consumption in multi-apartment buildings, three influencing factors must be considered: the condition of the building, technology-based solutions, and occupant behaviour. Studies show that in more developed countries, energy consumption of buildings can be reduced by 30 % to 80 % if energy efficiency is improved. Technology based solutions aim to reduce heat consumption, improve resource productivity, or replace outdated technologies, but these solutions often require large investments. Occupant behavior contributes to nearly 80 % of the variation in energy use. One of the ways to achieve these goals is to improve energy efficiency in multifamily buildings, because according to available literature, residential and commercial thermal energy consumption accounts for up to 20 % of total energy consumption in underdeveloped countries and up to 40 % in more developed countries. Changing people’s habits not only reduces heating energy consumption, but also does not require large investments for implementation.

Impact of green retrofits on heating energy consumption of apartment buildings based on nationwide energy database in South Korea

In South Korea, apartment buildings account for 63% of residential buildings and approximately 40% of apartment buildings were built before 2000, which leads to the growing demand for green retrofits of old apartment buildings. In this study, heating energy consumption of apartment complexes in Seoul and Busan was analyzed using a nationwide database, in order to evaluate the current energy status of apartment complexes. The energy consumption trend with building age were analyzed by classifying the heating bill data into four groups by construction year of apartment complexes. Based on these data, a simulation model of an old apartment building was developed to predict energy performance improvement through green retrofits. The results clearly showed that heating energy consumption increased with building age regardless of heating degree days in both regions. In particular, a significant difference in energy consumption was found in apartment complexes constructed before and after 2000, which implied the necessity of green retrofits for old apartments. The results of green retrofit simulation showed that heating energy consumption was reduced by up to 31.6% and 32% in Seoul and Busan, respectively. The method and results of this study can be applied to establish a green retrofit strategy based on a nationwide database of residential energy.

Energy saving and peak load shifting performance of tail water source heat pump integrated with large-scale thermal storage pool space heating system in technology park

In cities, there are thousands of wastewater treatment plants. The treated tail water drained from them is clean relatively, and has a stable flow rate. Extracting heat from it through tail water source heat pumps (TWSHP) for space heating can decrease the building heating energy consumption significantly. Besides, by integrating with thermal energy storage (TES), it can realize the goal of peak load shifting, especially in commercial buildings. However, there is few reports on its experimental investigations or engineering applications till now. To promote the application of TWSHP integrated with TES system, it was designed for the space heating of a technology park in Beijing, and a control method was proposed to decrease the operating cost by using the valley electricity. Then, utilizing its operating parameters in the heating season of 2021–2022, its heating performance was analyzed. Results indicate that the proposed system has good heating performance. The average COPs of TWSHP units and the system in the heating season are 7.08 and 4.41, respectively. Compared with gas-fired boiler, its carbon emission decreased by 62.53%. By using the TES system, the power consumption during valley period of power grid increases by 581.23%, realizing the goal of peak load shifting.

Study on the operation strategies and carbon emission of heating systems in the context of building energy conservation

AbstractCoal‐fired thermal power must be flexible to enable the grid absorption of inconsistent photovoltaic (PV) and wind power. Combined heat and power (CHP) coal‐fired plants are the primary source for district heating systems. This paper uses a 330 MW subcritical CHP unit as an example to carry out the study. With the promotion of building energy efficiency, when the thermal index is reduced to below 20 W/m2, the low‐load operation of CHP can meet the wind power and PV feed‐in demand and guarantee residential heating without the need for flexibility modification. Meanwhile, more renewable energy generation can reduce carbon emissions from the power supply, further contributing to reducing carbon emissions from buildings. The impacts of different envelope parameters and supplementary heat sources on building carbon emissions are also studied. The conclusion shows that the degree of their impact on carbon emissions ranks as ESMs (energy supply modes) > Factor D (infiltration N50) > Factor A (external wall heat transfer coefficient) > Factor C (window heat transfer coefficient) > Factor B (roof heat transfer coefficient). When the building's heating energy consumption gradually decreases, the distributed heat pump unit can replace the coal‐fired boiler to supply the peak heat load demand. In the future, China's district heating systems can be gradually changed from the current CHP and coal‐fired boilers to CHP and distributed heat pumps.

The Effect of Degradation on Cold Climate Building Energy Performance: A Comparison with Hot Climate Buildings

The issues of reducing energy consumption in buildings and their decarbonisation are currently among the most pressing. However, such an important aspect of the problem under discussion as the impact of unavoidable degradation processes on energy demand in buildings remains poorly understood. In addition, there are only a limited number of practical guidelines that can be used to take this factor into account at the design stage and during the further operation of buildings. The aim of this work was to assess the potential impact of component degradation and ageing on heating energy consumption in buildings, including insulated glass units, thermal insulation, airtightness, heat recovery of mechanical ventilation systems, and photovoltaic modules. The detached and apartment buildings were considered to be in a cold climate in the context of the Republic of Belarus. The study was based on simulation research using EnergyPlus. As a result, it was found that a possible increase in heating energy consumption might reach 17.6–61.2% over 25 years in detached houses and up to 23.6–89.8% in apartment buildings. These indicators turned out to be higher than the previously identified values for cooling energy consumption in a hot–humid climate. Based on the findings, recommendations for considering the degradation factor in cold climates in practice were developed, which were compared and integrated into the author’s existing guidelines.

Ensemble learning-based approach for residential building heating energy prediction and optimization

Accurate building energy consumption prediction is critical for engineers to design optimized operational strategies for building heating, ventilation, and air-conditioning systems. In this paper, an stacking ensemble learning-based model is established based on the operational data of a district resident buildings heating station for building heating system energy consumption prediction. The ensemble model is optimized by outlier processing, feature selection, parameter optimization based on grid search. A new feature based on Exponentially Weighted Moving Average (EWMA) algorithm was proposed to take historical energy feature into consideration. The performance of the ensemble model and four base machine learning methods, including multiple linear regression, extreme learning machine, extreme gradient boosting and support vector regression, are evaluated. Compared with the four base models, the Mean Absolute Error (MAE) of the ensemble model decreases by 4.36%–71.70%, and the Root Mean Squared Error (RMSE) by 3.80%–49.73%. Using the new feature based on EWMA can further reduce the MAE and RMSE of the ensemble model by 10.36% and 19.89%, respectively. The result proves that the proposed ensemble model with the added historical feature effectively improves the prediction model's accuracy for building heating energy consumption.

Experimental and numerical investigation of novel light weight concrete panels made with aerogel and phase change materials

Silica Aerogel (SAG) mortar is an ultra-lightweight cementitious composite. It has high heat-insulating properties, which helps to reduce building heating energy consumption. However, it may also increase overheating and peak cooling demand in buildings during summer due to low thermal mass to store thermal energy. Therefore, this study aimed to increase the thermal energy storage capacity of silica aerogel lightweight concrete panels (SAGP) by integrating Capric Acid (CA) Phase Change Materials (PCM). The CA was absorbed in Hydrophobic Expanded Perlite (HEP) porous materials to develop form stable capric acid hydrophobic expanded perlite (CAHEP) composite to prevent PCM leakage when melted. Various lightweight Heat Resistive and Storage Panels (HRSP) panels were developed by replacing sand with SAG and CAHEP composite at different volume fractions. Then, the thermophysical properties and thermal performance of those HRSPs were evaluated using appropriate standards and established methods. The results showed that the integration of 60% CAHEP in SAGP resulted in 43% and 56% higher thermal storage and inertia, respectively, compared to the reference SAGP without CAHEP. The developed HRSPs also have acceptable compressive strength and drying shrinkage for thermal insulating and plastering applications. Numerical simulation showed that the application of HRSPs on the interior surface of a typical Victorian house reduced annual energy use and greenhouse gas emissions by 9.14% and 24%, respectively, in air-conditioned buildings; and reduced summertime thermal discomfort hours by 42 h in a free running house during a heatwave period. Finally, the life cycle assessment revealed that HRSPs have lower environmental impacts than SAGPs. Thus, HRSPs can be a sustainable alternative to lightweight concrete panels.

Occupant behavior effects on energy-saving measures and thermal comfort in severe cold areas

The complexity of human behavior is often underestimated when evaluating building heating energy consumption, especially in severe cold areas. Therefore, this study aimed to examine the heating energy consumption and thermal comfort of different modes of occupant behaviors by focusing on Inner Mongolia, a cold region, as the research object. Using the results of a questionnaire survey to determine the activity of typical families in this region, five occupant behavior models were constructed. On this basis, simulations were conducted to determine the energy consumption of different behavior patterns using DeST-h software. The findings show that the effect of energy savings is much more significant than the loss of thermal comfort. However, not all energy-saving measures are effective, as different external temperatures cause differences in the energy consumption results of varying behavior patterns. Specifically, when the outside temperature is high, excessive energy-saving behavior is not effective for overall energy savings, and when the outside temperature is low, energy-saving actions, such as indoor heat preservation and zone temperature control, provide significant results. On this basis, strategies were identified to achieve a balance between energy-saving measures and thermal comfort in buildings using gas wall-hanging furnaces in cold region areas.

Demand response with active phase change material based thermal energy storage in buildings

A large potential to shift the electricity consumption to adapt to the stochastic renewable electricity generation is identified through the utilisation of a combination of Heat Pumps (HP) and local Thermal Energy Storage (TES) devices in building heating systems. In this paper, a building heating system coupled with an active Phase Change Material (PCM) TES device and a HP is simulated to characterise its potential for Demand Response (DR) applications. A control-oriented numerical model for the PCM TES is developed and previously validated numerical models of the building, HP and hot water radiators are integrated to simulate the dynamics of the coupled building heating system. A Genetic Algorithm (GA) based control strategy is designed to optimise the building heating energy consumption and operational cost with respect to time-varying electricity price signals. The developed control strategy is successfully implemented to utilise the TES capability of the PCM and the thermal inertia of the building to intelligently shift the electrical load of the HP to low price periods, while satisfying the specified indoor comfort requirements. In comparison to a reference case utilising a sensible TES, cost savings and consumption reductions of more than 40% and 30%, respectively, are attained with the active PCM TES. Simulation results indicate that utilising an active PCM TES over a sensible TES offers significant advantages for DR applications in building heating systems in terms of load shift flexibility, energy costs and consumption.

Optical evaluation of a smart transparent insulation material for window application

Thermotropic (TT) materials can provide dynamic regulation of solar energy and daylight, thus achieving building energy saving and indoor environment improvement. Parallel Slats Transparent Insulation Materials (PS-TIM) when integrated into the cavity of a double-glazed window can increase the thermal resistance of the window system, thus reducing building heating energy consumption. In this study, these two advanced technologies are combined to form a novel TT PS-TIM window system for adaptive daylighting control and enhanced thermal insulation. To achieve this, thermotropic hydrogels such as hydroxypropyl cellulose (HPC) and poly(N-isopropylacrylamide) (PNIPAm) are proposed for integration within polymethyl methacrylate (PMMA) slats between double glass panes. The optical properties of the PMMA-TT slats have been firstly investigated by optical simulations and experiments. Windows integrated with the PMMA-TT slats have been developed and predicted for their daylight performance under different solar incident angles, slat tilt angles and concentrations of HPC/PNIPAm polymer within hydrogel. It was found that the TT PS-TIM window can effectively reduce visible and solar transmittance when the TT material switches from clear (at low temperatures) to translucent (at high temperatures). In addition, the translucent TT PS-TIM window allows less light to be transmitted into the interior space when the slat tilt angle is increased from horizontal to inclined. More uniform daylight distribution in the indoor space can be achieved by increasing the slat tilt angle. The optical investigation results of this research will provide guidance for the further development of this system.

Analysis of Time-Sharing and Zoning Heat Load Demand and Research on Heating Control of Large Heating Buildings

It is of practical significance to analyze the time-sharing and zoning heat load demand and fine heating control of large heating buildings in order to reduce the building energy consumption. The existing research lacks the time-sharing and zoning form of refined heating for different functional zones of large heating buildings. For this reason, this paper carried out the time-sharing and zoning heat load demand analysis and research on heating control of large heating buildings. First, the mathematical model for the main heating links of the heating system of the large building based on the heat balance equation is constructed to realize the simulation of the dynamic change of the performance of the large building heating system. Then, the size, characteristics and influencing factors of heat load of various heating units in the building are analyzed, and three possible ways of mass regulation, mass-flow regulation and intermittent regulation that can change the flow by stages are introduced. The experimental results verify the effectiveness of the proposed heating control mode, which can effectively reduce the building heating energy consumption.

Study on the influence of thermo-physical parameters of phase change material panel on the indoor thermal environment of passive solar buildings in Tibet

In this study, an unsteady heat transport numerical model of phase change material (PCM) panel under indoor-outdoor dual thermal disturbance conditions was development by using the apparent heat capacity method, which was employed to evaluate the effect of thermal performance parameters of PCM on the heat storage and temperature regulation capability of PCM panel. Then, a single-space passive solar building located in Tibet was used as a basic building model to study the influence of thermo-physical parameters, such as phase change temperature, thickness and position of PCM panel in composite wall on indoor thermal environment. Meanwhile, the indoor discomfort degree hours and heating energy consumption of the passive solar building with composite wall containing PCM panel were quantitatively analyzed, and the dynamic payback period of PCM investment in Tibet was calculated for economic analysis. The results indicated that the phase transition temperature, thickness and position of PCM panel have a significant impact on its heat storage and temperature regulation capability, and the composite wall containing PCM panel with reasonable thermo-physical parameters could obviously damp indoor air temperature fluctuation and save the building heating energy consumption. Compared with the basic building, applying a 30 mm thickness PCM panel with phase transition temperature of 18 °C on the interior side of the composite wall could reduce indoor discomfort degree hours in studied period (Dec. 20 to Dec. 24) by 74.9% and reduce heating energy consumption in the whole heating period (Dec. 01 to Mar. 01) by 20.76%, respectively. Furthermore, the economic analysis results show that the application of PCM panels in passive solar buildings in Tibet has high economic value, and the dynamic payback period of PCM investment in Tibet with 3% discount rate is 41.29 years, which is lower than the usual service life of a building.

Development of a variable water temperature control method for air source heat pump based on the supply–demand balance

Air source heat pump (ASHP) is one of renewable energy technologies, and has been widely used for space heating. In practice applications, the setting value of its supply water temperature is long-term fixed, resulting in the poor coefficient of performance (COP) under partial load conditions. To improve the COP of ASHP, a real-time variable water temperature (VWT) control method for its supply water temperature was proposed in this work. In this control method, the optimal supply water temperature was calculated based on the supply and demand balance, and used as the setting value of supply water temperature. Then using an ASHP-radiant floor heating system, the feasibility and energy-saving performance of the proposed control method were investigated through experiment and simulation. Results indicate that the ASHP heating system can run smoothly when adopting the proposed VWT control method. Compared with the results in constant water temperature (CWT) control method, in the whole heating season, the COP of ASHP unit in VWT control method is 22.88% higher, while the building heating energy consumption is 26.16% lower. Besides, the percentage of predicted mean vote (PMV) between − 0.5 and + 0.5 increases from 48.61% in CWT control method to 87.86% in VWT control method.

Local thermal comfort-based optimal design of attached sunspace with breathing window for farmhouses in North China

Passive building with solar energy absorption for winter heating is an effective way to reduce building heating energy consumption. In order to solve the problems of uneven indoor temperature distribution and poor thermal comfort that exist in traditional attached sunspace heating, the Attached Sunspace with Breathing Window (ASBW) is studied in this paper. A field investigation and numerical simulation have been conducted on a farmhouse in North China to monitor the variation of indoor temperature. On this basis, three more contemporary ASBW design schemes are proposed, and the characteristics and benefits of the Attached Sunspace with Breathing Window are further studied. The Percentage Dissatisfied (PD) is selected as the index of indoor local thermal comfort to evaluate the performance of the ASBW. The results show that the indoor temperature in the vertical direction increase with the height, with the high-temperature region exceeding 23 °C and the low-temperature region only about 18 °C on sunny days. Simulation analysis of the three schemes revealed that the Bottom outlet scheme and the Top inlet scheme possess better local thermal comfort. The maximum average indoor temperature of both schemes is around 23 °C, and the vertical indoor temperature PD index is significantly reduced to the lowest average value of only about 8% compared to the original design with a single-window of 23.6%. The Bottom outlet scheme and the Top inlet scheme are recommended for the newly-built and existing buildings, respectively.