Building Thermal Loads Research Articles

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Dynamically coupled modelling of ground source heat pump systems considering groundwater flow and unbalanced seasonal building thermal loads

The ground source heat pump (GSHP) system is a promising technology to exploit shallow geothermal energy via ground heat exchangers, GHEs (e.g., deep boreholes or piles) for space heating or cooling. Although GSHP systems have been widely used in temperate regions owing to their environmentally clean characteristics and excellent energy-saving performance, their use in cooling-dominated areas, such as subtropical or tropical regions (e.g., Hong Kong or Singapore), remains limited. The operation of GSHP systems in these regions may be subjected to unbalanced seasonal thermal loads on buildings and induce heat accumulation in soils surrounding GHEs, leading to potential deterioration in the long-term performance (e.g., coefficient of performance, COP) of GSHP systems. To properly evaluate the long-term COP of GSHP systems in cooling-dominated areas, a dynamically coupled simulation approach is proposed in this study. The proposed method integrates building thermal loads with ground heat transfer under groundwater seepage flow, within a unified framework. The effectiveness of this approach is demonstrated through a case study where, in the absence of groundwater seepage flow, the soil temperature increased by 17.2 °C. In contrast, when groundwater seepage flow was considered, the GSHP system not only maintained a high long-term cooling COP but also achieved up to 30 % electricity savings. These findings demonstrate the feasibility and sustainability of applying GSHP systems in cooling-dominated areas with the aid of groundwater seepage flow.

Multi-objective optimization of buildings in urban scale for early stage planning and parametric design

Passive design of buildings in early design stage is essential for energy efficiency and sustainability. In this study, a streamlined tool for parametric study and passive design of buildings in urban scale considering thermal loads and natural lighting performance is presented, which markedly enhances the efficiency of early-stage urban planning. The tool is comprised of three sub-models, including (1) degree-month based annual building thermal load prediction model, (2) simplified climate modification model that takes into account the effects of building layout on solar heat gain and urban heat island, (3) multi-objective optimization algorithm NSGA-II, which optimizes both energy-saving and natural lighting performance. Model validation results show that this tool has an average error of 11.44% and 15.82% in simulating building thermal load compared with Dragonfly and CitySim, and has an average error of 9.9% in predicting average daylight factor compared with Radiance. Multiple optimization scenarios and sensitivity analysis in 10 typical climatic cities were conducted. It is found that in cold and hot climatic cities, it is more beneficial to set energy target as the optimization objective, meanwhile, establishing natural lighting as a constraint, rather than solely minimizing thermal loads. In contrast, the inherently lower thermal loads of buildings in milder climatic cities help to achieve both energy-saving and natural lighting goals, and it is also found that the increased thermal loss due to a higher window-to-wall ratio can be offset by employing thicker wall and roof insulation or by improving building layouts.

Experimental investigation of a distributed photovoltaic heating system based on building envelope thermal storage

Low-carbon heating in farmhouses can be achieved using photovoltaic (PV) heating. However, the severe mismatch between the PV energy supply and the building thermal load requires expensive energy storage devices and control systems. In this study, a controller-less PV heating system utilizing the building envelope for thermal storage was evaluated on a farmhouse in northern China. The building envelope simultaneously functions as the heating terminal and is composed of insulation boards, electric heating wires, and the thermal storage wall. PV electricity is fed to be absorbed by the electric heating wire to produce heat. The thermal storage wall absorbs the heat generated by the heating wire before releasing it into the room through convection and radiation. Long-term monitoring reveals that the black bulb temperature of the farmhouse was above 16 °C for more than 60 % of the time throughout the heating season. The building envelope acting as a thermal reserve improved system supply and demand matching by 77 % and the indoor temperature fluctuation was reduced by about 47 %. The thermal storage wall can provide 58 % of the heating energy when there is no PV power supply. The payback period of the heating system is only 6.5 years, verifying the good rate of return of the system. This study proposes a lower cost energy storage solution for PV heating than previous studies, and engages the building envelope into the building energy system.

The potential of utilizing vertical borehole heat exchangers in residential buildings for the various climate zones of Saudi Arabia

Most of the literature concerning geothermal energy in Saudi Arabia has focused on power generation applications. This paper investigates the potential of utilizing ground-source heat pump (GSHP) with vertical borehole heat exchangers in residential buildings in the various climate zones and subsurface geologies of Saudi Arabia. Thermal loads of a typical residential building in Saudi Arabia were generated using eQuest software, then used in a model of GSHP system in TRNSYS. The performance and economics of the GSHP system was evaluated and compared with a typical air-source heat pump (ASHP) system for each location. The total borehole lengths in all zones were determined using the cooling load (the dominant load). The potential for employing GSHP systems was found to be not uniform across Saudi Arabia. The required length of the GHE ranged between 12 and 148 m/kW of cooling. The annual energy saving when employing GSHP instead of ASHP systems varied between 3 % to 19 %, and the building's electricity peak demand could be reduced between 5 % to 43 %. Although GSHPs reduced electrical and maintenance costs, their high drilling cost makes them economically unattractive under the present electric utility charge in Saudi Arabia.

Multiscale analysis of a seasonal latent thermal energy storage with solar collectors for a single-family building

This paper presents a comprehensive system-to-CFD multiscale analysis of a seasonal thermal energy storage (STES) system based on phase change materials (PCMs) for efficient energy storage and release for space heating. The study investigates the impact of various factors, including the geometry of the individual storage tank and the thermo-fluid dynamic parameters of the heat transfer fluid on the system performance. The work focuses on STES integration with space heating system to meet the thermal energy demands of a typical single-family building located in Naples – South Italy, typical coastline Mediterranean climate. The primary goal is to assess the behaviour of the system-integrated PCM tank and its characteristic parameters, leading to the proposal of sustainable approaches that include renewable energy sources and partially fulfil the building thermal loads. Among the Scenarios, one stands out as it achieves a better operation, allowing storage and solar collectors to cover around 69.2 % of the winter heating demand, i.e., 3.06 MWh. The study also discusses the benefits of modular system operation, as evidenced by Scenario 3, which allows for the parallel discharge of multiple LHTES tanks, leading to an enhancement in efficiency compared to other Scenarios. Results indicate that the total energy discharged by a single STES is 56.30 kWh, with a latent heat fraction released over a time of 111 h and an average temperature difference of 5.65 °C. The tank efficiency reaches a value of 90.4 %, demonstrating the effectiveness of the STES system in meeting heating demands.

Seasonal performance of an energy pile heat pump system and prediction of building thermal load

Ground source heat pumps (GSHPs) are widely used for cooling and heating buildings, which can reduce the burning of fossil fuels and contribute to the reduction of carbon emissions. Energy piles, as underground heat exchangers, have advantages over conventional buried borehole pipes, saving drilling costs and accelerating the construction period. However, few studies have focused on the actual operation of an energy pile heat pump system, and the prediction of the building thermal load can reduce the instrument arrangement and test recording time. Hence, a heat pump system was built in this study to serve a single lab using energy piles with a cap structure as the energy supply, and summer cooling and winter heating tests were conducted. Under the test conditions in this study, the GSHP had a faster start-up speed and less power consumption than the air source heat pump (ASHP), and the average coefficient of performance (COP) of the energy pile heat pump system was 3.78, which is 40.0 ∼ 51.2% higher than that of the corresponding ASHP (the normative reference COP is 2.50 ∼ 2.70). The methods of higher-order surface fitting and an artificial neural network model were both suitable for predicting building thermal load.

Heating performance analysis of solar energy-medium deep buried pipe

In order to promote the vigorous development of renewable energy. Regarding the thermal load of buildings in Yulin mining area. Establish a solar deep buried pipe system using TRNSYS software. The feasibility of the components is verified by experiments. After comparing three different operating systems, it is concluded that the solar-medium and deep buried pipe system has more advantages. By simulating the solar energy - deep buried pipe system, it is concluded that: (1) The water supply temperature is relatively stable and can be maintained at 48 °C. (2) The COP can be up to 6.2, and the heat supply can also meet the building demand. (3) The cost is also low after long - term operation. Therefore, it is feasible to use solar energy-deep buried pipe system in mining area.

Surrogate modeling for long-term and high-resolution prediction of building thermal load with a metric-optimized KNN algorithm

• Surrogate modeling, to predict all-year hourly building loads in pre-design stage. • Load database contains 16384 models with crucial features and thermal load data. • Manhattan distance is the optimal metric when performing KNN in load prediction. • Propose thermal load prediction process at district level and apply it to a case. During the pre-design stage of buildings, reliable long-term prediction of thermal loads is significant for cooling/heating system configuration and efficient operation. This paper proposes a surrogate modeling method to predict all-year hourly cooling/heating loads in high resolution for retail, hotel, and office buildings. 16 384 surrogate models are simulated in EnergyPlus to generate the load database, which contains 7 crucial building features as inputs and hourly loads as outputs. K-nearest-neighbors (KNN) is chosen as the data-driven algorithm to approximate the surrogates for load prediction. With test samples from the database, performances of five different spatial metrics for KNN are evaluated and optimized. Results show that the Manhattan distance is the optimal metric with the highest efficient hour rates of 93.57% and 97.14% for cooling and heating loads in office buildings. The method is verified by predicting the thermal loads of a given district in Shanghai, China. The mean absolute percentage errors (MAPE) are 5.26% and 6.88% for cooling/heating loads, respectively, and 5.63% for the annual thermal loads. The proposed surrogate modeling method meets the precision requirement of engineering in the building pre-design stage and achieves the fast prediction of all-year hourly thermal loads at the district level. As a data-driven approximation, it does not require as much detailed building information as the commonly used physics-based methods. And by pre-simulation of sufficient prototypical models, the method overcomes the gaps of data missing in current data-driven methods.

Energy, economic, and carbon emission analysis of a residential building with an energy pile system

This paper investigated the combined energy, economic and carbon performances of energy piles used for heating and cooling buildings, an issue that has received limited interest in the past. To address this knowledge gap, a 30-year numerical simulation, calibrated using field data, was conducted for variable building thermal loads, pile spacing, pile length and number of energy piles for a six-storey residential building in Melbourne, Australia. The energy pile system in the annual heating-only mode demonstrated a 75 % reduction in energy consumption and a 5 % reduction in energy costs compared to a natural gas boiler. The energy piles performances in the annual heating-and-cooling mode were significantly higher than in the heating-only mode, demonstrating a 39 % reduction in energy consumption, carbon emissions and energy costs compared to an air-source heat pump. Increasing the pile spacing from 2.05 m to 6.4 m, pile length from 10 m to 20 m, and number of energy piles from 38 to 114 increased the energy, economic and carbon benefits of energy piles by 76 %, 77 % and 119 %, respectively. The results highlight the importance of building thermal loads and pile configuration on assessing the energy, economic and carbon performances of energy piles during early design stages.

Prediction and Optimization of Thermal Loads in Buildings with Different Shapes by Neural Networks and Recent Finite Difference Methods

This study aimed to estimate the heating load (HL) and the cooling load (CL) of a residential building using neural networks and to simulate the thermal behavior of a four-layered wall with different orientations. The neural network models were developed and tested using Multi-Layer Perceptron (MLP) and Radial Basis (RB) networks with three algorithms, namely the Levenberg-Marquardt (LM), the Scaled Conjugate Gradient (SCG), and the Radial Basis Function (RB). To generate the data, 624 models were used, including six building shapes, four orientations, five glazing areas, and five ways of distributing glazing. The LM model showed the best accuracy compared to the experimental data. The L-shape facing south with windows on the east and south sides and a 20% window area was found to be the best shape for balancing the lighting and ventilation requirements with the heating and cooling loads near the mean value. The heating and cooling loads for this shape were 22.5 kWh and 24.5 kWh, respectively. The simulation part used the LH algorithm coded in MATLAB to analyze the temperature and heat transfer across the wall layers and the effect of solar radiation. The maximum and minimum percentage differences obtained by HAP are 10.7% and 2.7%, respectively. The results showed that the insulation layer and the wall orientation were important factors for optimizing the thermal comfort of a building. This study demonstrated the effectiveness of neural networks and simulation methods for building energy analysis.

Applicability of a heat-pump-driven liquid-desiccant air-conditioning system in energy-efficient buildings

Building trend is toward energy-efficiency by reducing building sensible load, while neglecting latent load. A heat-pump-driven liquid-desiccant (HPLD) air-conditioning system has emerged for effectively handling building latent load in energy-efficient buildings with low indoor sensible heat ratio. However, previous studies only investigated one building type, failing to account for the increase in building energy-efficiency. This study estimates building thermal loads of four different energy-efficient high latent load buildings, simulating building trend. Then, as energy-efficiency of buildings increases, the changes in indoor thermal comfort and energy consumption of the HPLD air-conditioning system and reference vapour compression system are compared. Simulation results show that in building with the highest energy-efficiency, the reference system can only maintain thermal comfort for 43.3% and 24% of summer operating hours when primarily meeting supply-air target temperature and humidity, respectively. Contrastingly, the proposed system can maintain thermal comfort in all building types for almost 100% of summer operating hours due to decoupled control of air temperature and humidity. Additionally, the proposed system saves up to 33.2% of operating energy when both systems achieve the same thermal comfort satisfaction. In conclusion, the HPLD air-conditioning system with decoupled control is more applicable as energy-efficiency of buildings increases.

Low-Cost Distributed Thermal Response Test for the Estimation of Thermal Ground and Grout Conductivities in Geothermal Heat Pump Applications

The design process of a borehole heat exchanger (BHE) requires knowledge of building thermal loads, the expected heat pump’s COP and the ground’s thermophysical properties. The thermal response test (TRT) is a common experimental technique for estimating the ground’s thermal conductivity and borehole thermal resistance. In classic TRT, a constant heat transfer rate is provided above ground to the carrier fluid that circulates continuously inside a pilot BHE. The average fluid temperature is measured, and from its time-dependent evolution, it is possible to infer both the thermal resistance of the BHE and the thermal conductivity of the ground. The present paper investigates the possibility of a new approach for TRT with the continuous injection of heat directly into the BHE’s grouting by means of electrical resistance imparted along the entire BHE’s length, while local (along the depth) temperature measurements are acquired. This DTRT (distributed TRT) approach has seldom been applied and, in most applications, circulating hot fluid and optical fibers are used to infer depth-related temperatures. The distributed measurements allow the detection of thermal ground anomalies along the heat exchanger and even the presence of aquifer layers. The present paper investigates the new EDDTRT (electric depth-distributed TRT, under patenting) approach based on traditional instruments (e.g., RTD) or one-wire digital sensors. The accuracy of the proposed method is numerically assessed by Comsol Multiphysics simulations. The analysis of the data obtained from the “virtual” EDDTRT confirms the possibility of estimating within 10% accuracy both thermal ground and grout conductivities.

A real time peer-to-peer energy trading for prosumers utilizing time-varying building virtual energy storage

With the increasing penetration of distributed energy resources (DER) in the electric power system, Peer-to-Peer (P2P) energy trading has become a promising paradigm for future electric power systems. Building thermal load, which is an important demand side resource, should be considered carefully in the design of a P2P trading method. In this paper, we investigate the application of building thermal energy storage capability in P2P energy trading. We aggregate and model building thermal loads as a virtual energy storage and derive a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. Key parameters of T-VESS, including charging/discharging rate, energy capacity, and state of charge (SOC), are analyzed so that T-VESS can be embedded in the building prosumer model to participate effectively in electricity-oriented P2P energy trading. We propose a real time P2P energy trading method of prosumers based on model predictive control (MPC). This method consists of an energy supply and demand quantification stage and a transaction price optimization stage, which can effectively capture the time-varying nature of T-VESS. To preserve the trading preference for prosumers, we propose a distributed P2P energy trading actions implementation method based on the continuous double auction (CDA) considering multiple trading preference grades. Numerical results demonstrate that the proposed P2P energy trading method considering T-VESS can reduce the operational cost of prosumers by 3.7%, while also promoting the local integration of renewable energy by 3.1%. Furthermore, compared to existing P2P trading methods considering single preference grade, the proposed method greatly enhances the utilities of both individual prosumers and the overall community.

An artificial intelligence (AI)-driven method for forecasting cooling and heating loads in office buildings by integrating building thermal load characteristics

Due to the thermal inertia of building envelope and random uncertainty of occupant behaviors, real-time and accurate forecasting for building cooling and heating loads is not easy to implement. It requires not only favorable modeling methods, but also attention to the intrinsic cause analysis of loads. However, most previous studies have focused more on the improvement of forecasting algorithms, lacking the comprehensive analysis and scientific research from the load formation mechanism. To address this, a novel artificial intelligence (AI)-driven forecasting method integrating building thermal load characteristics, is proposed. This modeling approach takes into account both strong reflection ability of resistance-capacitance (RC) for the thermophysical characteristics of building envelope and the superior handling capability of dynamic Bayesian network (DBN) for occupant uncertainties. Therewith, the independent building envelope model and occupant behavior model are established. Coupling the predicted values of these two sub-models, the building total load forecasting model is established using fuzzy radial basis function (RBF) neural network. A case study of an office building in Tianjin is utilized to validate the proposed method, revealing excellent predictive performance for both the sub-models and the total load model. The proposed method can effectively predict thermal load both from the formation mechanism of internal and external disturbances. It was found that the AI-driven model incorporating building thermal load characteristics, achieves significantly higher precision compared to the general data-driven model, with a reduction of 14.74% in coefficient of variation of root mean squared error in summer and 59.78% in winter.

Temporal graph attention network for building thermal load prediction

Machine learning models have seen widespread application in predicting building thermal loads. Yet, these existing models generally predict the thermal load for a single thermal zone or an entire building, overlooking buildings with multiple thermal zones. Moreover, interactions between thermal zones are seldom accounted for these methods. Addressing this gap, our study introduces a graph-based approach that employs a Temporal Graph Attention Network (TGAT) model for building thermal load prediction. This model accommodates both the spatial and temporal dependence across different thermal zones. Specifically, the TGAT model initially utilizes graph attention neural networks (GATs) to identify the spatial relationships between thermal zones. The spatial representation thus obtained is subsequently processed via Gated Recurrent Units (GRUs) to ascertain temporal dependencies. Lastly, a fully connected layer is engaged to decode the representation for the final output. Our model incorporates 11 features as inputs to depict the characteristics of outdoor environment and thermal zones. Testing the proposed TGAT model in a small office virtual environment for heating load prediction revealed that a configuration of four GAT attention heads achieved optimal result for predicting the ensuing hour’s heating load with a six-hour lookback. Additionally, we scrutinized and discussed the efficacy of feature selection and the GAT module. This research thus establishes a novel groundwork for forthcoming studies on zone-based building energy management optimization.

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings.

As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO), for attaining optimal trainings. These five integrations aim at predicting the annual thermal energy demand. The accuracy of the models is broadly assessed using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) indicators and a ranking system is accordingly developed. As the MAPE and R2 reported, all obtained relative errors were below 5% and correlations were above 92% which confirm the general acceptability of the results and all used models. While the models exhibited different performances in training and testing stages, referring to the overall results, the BWO emerged as the most accurate algorithm, followed by the AOA and SO simultaneously in the second position, the FBI as the third, and TSO as the fourth accurate model. Mean absolute error (MAPE) and Considering the wide variety of artificial intelligence techniques that are used nowadays, the findings of this research may shed light on the selection of proper techniques for reliable energy performance analysis in complex buildings.

Short-term cooling and heating loads forecasting of building district energy system based on data-driven models

Accurate forecasting of cooling and heating loads is critical for optimizing the energy usage of devices and planning for energy storage in building district energy systems (BDESs). Data-driven models offering high prediction accuracy and efficiency are receiving significant attention for addressing short-term load prediction problems. However, load forecasting for district building levels, as well as interpretable studies of deep learning models, are still the minority of existed research. To satisfy the cooling and heating loads forecasting of an actual BDES, a framework that includes raw data acquisition, data processing, model development, and evaluation is developed and analyzed. The performance of a multi-input-multi-output prediction strategy evaluated using five data-driven models, namely extreme gradient boost (XGB), long short-term memory (LSTM), gated recurrent units (GRU), and attention-added LSTM/GRU, is investigated. The results show that XGB performs the best, with CV-RMSE values of 14.51% and 11.95% for the one-hour-ahead forecasting of cooling and heating loads, respectively. In terms of 24-hour ahead prediction, the most accurate condition is “48–24” attention-added LSTM in both cooling and heating loads forecasting, where the CV-RMSE values recorded are 26.61% and 27.58%, respectively. The accuracy and interpretability of the models improved after an attention mechanism is introduced. The most significant enhancement in the prediction accuracy is reflected by a reduction in the CV-RMSE by 2.01%. The visualized heat maps of attention weights indicate the mechanism by which the models learn and extract the load patterns, where LSTM and GRU indicate different priorities. Further analysis of the attention weight distribution reveals the effects of building thermal inertia and load periodicity.

Effects of Building Materials on Building Thermal Load in Malaysian Institutional Library

Green building, net-zero energy building, low energy building, and sustainable building are the common terms being used in the building industry nowadays, with the primary aim to produce buildings with low energy and low carbon dioxide (CO2) emissions. Different types of building materials can have significant implications for energy efficiency, especially in the warming climate. The energy crisis, future global warming, and climate change can be mitigated by choosing the right building materials, which will also lower the building's energy consumption and carbon dioxide emissions. This paper presents the effects of different building materials on the cooling capacity and cooling energy consumption of a building for four (4) different climate change weather data, namely, the present time scenario, the 2020s, 2050s, and 2080s time scenario using TRNSYS simulation software. The main library of a university in Petaling Jaya, Malaysia, was chosen as the case study. Three (3) types of building materials were studied, namely, Type 1 (brick walls with single-glazed windows), Type 2 (foam insulation cavity walls with double-glazed windows), and Type 3 (air gap cavity walls with double-glazed windows). The simulation results showed that the Type 2 building materials could reduce the yearly cooling energy consumption by 14.5%, compared with the current building materials (brick walls with single-glazed windows). The Type 3 building materials (air gap cavity walls with double-glazed windows) with the lower installation were wound to reduce the yearly cooling energy consumption by 9% compared with the Type 1 building materials. For architects, designers, politicians, and library administrators, the research's conclusions have immediate practical implications that will help them make decisions and implement energy-saving plans. In the end, this research helps libraries in Malaysia remain sustainable and resilient in the face of global warming and the energy crisis.

Adaptive thermal load prediction in residential buildings using artificial neural networks

Accurate prediction of thermal load in buildings is essential for efficient energy planning. In this study, we investigate the application of Artificial Neural Networks (ANNs) to predict thermal load and indoor temperature evolution in residential buildings. We propose a flexible and adaptive model that is retrained on a daily basis to deliver updated hour-by-hour predictions for the following 24 h. To strike a balance between prediction accuracy and computational efficiency, we employ various design choices, including feature selection using Pearson's correlation coefficient (PCC), dynamic architecture, down-sampling, and specific state resetting and weight initialization. The results demonstrate the efficacy of our proposed model, as indicated by low root mean square error (RMSE) and mean bias error (MBE) values. For zone temperature, the average RMSE and MBE are 0.3 °C and 0.18 °C (summer) and 0.5 °C and 0.2 °C (winter), respectively. Furthermore, the average RMSE and MBE for thermal load predictions are 12 W/m2 and -2.4 W/m2 (winter) and 10 W/m2 and -0.6 W/m2 (summer), respectively. These performance metrics establish our model as a valuable tool for optimizing heating and cooling systems, resulting in energy savings and cost reductions. Our findings emphasize the potential of ANNs for precise thermal load and indoor temperature predictions, offering practical implications for building operators, engineers, and researchers involved in energy management.