Energy Consumption Prediction Research Articles

Decarbonization of Heating and Cooling Systems of Buildings Located Nearby Surface Water Sources: Case Study

The study was carried out to evaluate theoretically and in laboratory conditions the capacity of a hybrid heating and cooling system that sustainably uses thermal energy extracted from surface waters in order to decarbonize buildings located near water sources. The novelty of the research consists in the realization of two experimental systems, one for the rapid evaluation of the performance of the water–water heat pump heating system and one for the evaluation of the operating behavior of a cooling system with fan coil units. Starting with the heating and cooling demand, and the climatic and hydrological local characteristics, a hybrid system model for the heating and cooling of the analyzed building was established and implemented. The forecasted energy consumption and CO2 emissions for the operation of the new equipment were compared with the historical values of the old systems with which the building was equipped (thermal energy supply from the district heating and cooling system with an air conditioning unit). Also, the results were extrapolated for forecasting the energy potential of the surface waters. The study highlights a percentage reduction in annual energy consumption of 67.71% and CO2 emissions of 80.13% through the implementation of the hybrid system.

Enhancing Energy Consumption Prediction in Smart Homes: A Convergence-Aware Federated Transfer Learning Approach

Achieving accurate energy consumption prediction can be challenging, particularly in residential buildings, which experience highly variable consumption behavior due to changes in occupants and the construction of new buildings. This variability, combined with the potential for privacy breaches through conventional data collection methods, underscores the need for novel approaches to energy consumption forecasting. The proposed study suggests a new approach to predict energy consumption, utilizing Federated Learning (FL) to train a global model while ensuring local data privacy and transferring knowledge from information-rich to information-poor buildings. The proposed method learns the transferable knowledge from the source building without any privacy leakage and utilizes it for target buildings. Since the performance of the global model could be negatively affected by some participating nodes with poor performance due to noisy or limited data, we propose a client selection strategy on the server based on the normal distribution for choosing the best possible participants for the global model. Our method enables clients to participate selectively in the aggregation procedure to avoid model divergence due to poor performance. The proposed model is evaluated and conducts in-depth analyses of energy consumption patterns. We validate the performance by comparing its MAE, MSE, and R2 values to those of existing traditional and ensemble models. Our findings indicate that the proposed FL-based model with selective client participation outperforms its counterpart methods regarding predictive accuracy and robustness. The source code is available on Github

A Sustainable Development for Building Energy Consumption Based on Improved Rafflesia Optimization Algorithm with Feature Selection and Ensemble Deep Learning

Buildings emit a great deal of carbon dioxide and use a lot of energy. The study of building energy consumption is useful for the sustainable development of multi-energy planning and energy-saving strategies. Therefore, a sustainable development for building energy consumption based on the improved rafflesia optimization algorithm (ROA) with feature selection and ensemble deep learning is proposed in this paper. This method can explore data on building energy usage, assess prediction accuracy, and address concerns that building energy usage research must address. The proposed model first uses an improved self-organizing map with a new neighborhood function to select important features. After that, it uses ensemble deep learning to accurately anticipate the building’s energy usage. In addition, the improved ROA is used to fine-tune parameters for feature selection and ensemble deep learning. This research uses the dataset of the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) to compare the performance of several modeling approaches. It identifies the top five most important features based on the model’s results. Furthermore, the proposed model can be successfully applied to a real-world application. They both have the lowest root mean squared errors among the approaches examined. The proposed model indeed provides the benefits of feature selection and ensemble deep learning with the improved ROA for the prediction of building energy consumption.

A deep belief network-based energy consumption prediction model for water source heat pump system

To achieve optimal energy efficiency in buildings, accurately forecasting the energy consumption of air conditioning systems is crucial. This study develops an energy consumption prediction model based on a deep belief network, which is constructed according to the principles of a restricted Boltzmann machine. Actual experimental data from a water source heat pump system are collected, and feature variables are selected. The study discusses the impact of model parameters and training set sizes on the performance of energy consumption prediction model. Additionally, the trend in model prediction performance is analyzed through parameter adjustments. The results show that the coefficient of determination (R2) for the optimized model has increased to 0.585. The mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) have been reduced to 6.311, 2.512, and 1.625, respectively. The deep belief network energy consumption prediction model outperforms other common machine learning models for water source heat pump systems.

Development of an ARIMAX model for forecasting airport electricity consumption in Accra-Ghana: The role of weather and air passenger traffic

The rapid expansion of the aviation industry has significantly increased energy consumption within airport terminals. This study introduces an advanced ARIMAX model to forecast electricity consumption at Kotoka International Airport (KIA) in Ghana by incorporating weather conditions and air passenger numbers. By analysing data from 2019 to 2022, including lockdown periods, the model demonstrates superior predictive accuracy, evidenced by an RMSE of 55,914 kWh, a MAPE of 4.22 %, and an R-squared value of 0.899. Key determinants of energy consumption identified include air passengers and temperature, highlighting their substantial impact on energy use. The ARIMAX model's accuracy underscores its potential for enhancing energy management practices at airports. By integrating variables such as air passenger traffic and weather conditions, the model refines energy demand predictions, supporting more efficient energy use and operational planning. The ARIMAX model proves to be a valuable tool for forecasting energy consumption at KIA, aiding in energy management and sustainability efforts.

Optimal active and reactive energy management for a smart microgrid system under the Moroccan grid pricing code

This article presents an innovative active and reactive energy management system (AR-EMS) specifically designed for residential buildings in Morocco, seamlessly integrated with a Smart Microgrid (SMG) and the Electrical Power Grid (EPG) supplier. The considered SMG incorporates a Photovoltaic system (PV) and an Energy Storage System (ESS). In Morocco, the EPG utilizes a Range Tariff System (RTS), where the tariff structure depends on the total energy consumed at the end of each month. Hence, the proposed AR-EMS is designed to computes optimal active and reactive power setpoints of each source at 15-minute intervals for the next 30 days, based on forecasts of energy consumption and PV power production. The AR-EMS consists of two distinct layers: the Long-Term Optimization Layer (LTOL) and the Short-Term Optimization Layer (STOL). The LTOL focuses on optimizing active and reactive power flow over an extended time horizon of 30 days, relying on forecasted data. In contrast, the STOL involves short-term forecasting and energy optimization over the next 24-hour horizon, addressing the LTOL forecasting uncertainties. The proposed strategy emphasizes a multi-objective problem aiming at minimizing both active and reactive energy bill, ESS degradation cost, Peak-to-Average Ratio (PAR) and carbon emissions. The considered problem is solved using a metaheuristic algorithm, specifically the Lexicographic approach within the Particle Swarm Optimization (PSO) method. To validate the effectiveness of the proposed approach, two comparative analyses are conducted. The first involves the impact of time horizons on the energy cost and the management performances within the Moroccan context. The second comparison, highlights the benefits and shortfalls of using simultaneous active and reactive power management, comparing it with a system exclusively managing active power (A-EMS). Both compared strategies are evaluated using the Moroccan tariff RTS and real world data. The comparative analysis highlights the advantages of the proposed AR-EMS, demonstrating benefits in terms of active and reactive energy bill reduction by 58.9% and 83.2%, respectively, and showcases a 49.3% reduction in carbon emissions while improving the EPG power factor by 6% compared to A-EMS, In conclusion, the optimization results are encouraging the Moroccan government to further develop the installation of SMGs.

Multiscale graph based spatio-temporal graph convolutional network for energy consumption prediction of natural gas transmission process

Energy consumption prediction is crucial for planning and managing natural gas networks, enhancing transmission efficiency. However, most of relevant studies fail to consider the topology of natural gas network. Therefore, a multiscale graph based spatio-temporal graph convolutional network model (MG-STGCN) is proposed, which can extract the multi-scale relationship of data. Based on graph convolutional and temporal gated convolution network, data correlation, spatial dependence and time dependence are considered to improve the prediction effect. In order to verify the effect of the model, the model is tested based on two natural gas networks in different region of China. The single step prediction results of MG-STGCN are significantly better than spatio-temporal graph convolutional network (STGCN), long short-term memory (LSTM), gate recurrent unit (GRU) and temporal convolutional network (TCN), which proves the effectiveness and generalization ability of MG-STGCN. The training time of MG-STGCN is reduced by 20.8 % compared with STGCN. In different prediction time step, the average prediction effect of MG-STGCN is better than that of other methods, and the performance improvement rate is not less than 8.6 %. MG-STGCN can well capture the parameter correlation, spatial and temporal dependence of data, and achieve effective prediction of pipeline energy consumption.

A hybrid transfer learning to continual learning strategy for improving cross-building energy prediction in data increment scenario

For a data-driven building energy prediction model, the insufficient data available for new buildings and existing buildings with limited data pose a challenge to achieving the expected prediction performance. Transfer learning (TL) and continual learning (CL) are two widely applied effective methods for addressing the shortage of building data. The actual building projects typically exist in a scenario of data increment, wherein their energy consumption data gradually accumulates with usage. Previous studies have indicated that the TL strategy can transfer the learned data distribution patterns from source buildings to target buildings of the same type. Meanwhile, the CL strategy can extract the dynamic characteristics from continuously accumulated data. Both strategies can be employed to enhance the performance of energy consumption prediction models for buildings with insufficient data. However, existing studies on data scarcity in energy prediction primarily focus on buildings with fixed data quantities, and have not provided sufficient analysis for newly constructed buildings in a data increment scenario. Therefore, this study proposes a TL-to-CL strategy to enhance the energy prediction performance of newly constructed buildings in a data increment scenario. When the available data is limited, TL should be utilized. But as the data volume increases to be sufficient, TL should be transformed to CL at the reasonable time. Additionally, this study provides a reference for determining the transform time for the TL-to-CL strategy. The validation was conducted using two years of energy consumption data from 36 buildings in the Genome Project 2 dataset. The results demonstrate that the proposed TL-to-CL strategy (transform time = 4-week) obtain the overall better prediction performance than pure TL, pure CL and combined TL-CL without transformation between the two) strategies. The prediction improvement ratio (PIR) can be as high as 0.9 compared with the traditional LSTM.

A novel optimized hybrid machine learning model to enhance the prediction accuracy of hourly building energy consumption

ABSTRACT Building energy consumption (BEC) prediction is crucial in efficient energy management. This paper proposes an optimized hybrid prediction model that combines a support vector regression (SVR), a newly evolved coati optimization algorithm (COA), and a recursive feature elimination with cross-validation (RFECV) implemented on an hourly new dataset. The SVR is selected based on the experimentations conducted in this work that outperform other models. The COA is used for optimizing the hyperparameters of SVR, and RFECV is used to optimize the dataset. The SVR COA performs better than the Harris hawk optimization and Gray wolf optimization. Later, the optimized SVR is implemented on the optimized dataset, showing better accuracy and faster prediction compared with the default SVR, SVR with feature elimination, and optimized SVR models. The error metrics MAE, MAPE, RMSE, and R2 are used for the model evaluation. The testing accuracy improved by 12.34%, 10.52%, 17.02%, and 0.09%, respectively, compared to the default model.

Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.

Energy Consumption Prediction in Residential Buildings—An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with Evolutionary Optimization

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy–interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms—in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs’ designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions.

Optimizing energy expenditure in agricultural autonomous ground vehicles through a GPU-accelerated particle swarm optimization-artificial neural network framework

The accurate energy consumption prediction in Agricultural Ground Vehicles (AGVs) holds immense potential for optimizing operational efficiency and minimizing environmental impact. However, existing optimization methods for such prediction tasks often suffer from high computational demands, hindering their practical implementation. This paper introduces a ground-breaking approach that overcomes this limitation by leveraging the potent computational power of Graphics Processing Units (GPUs) to accelerate the optimization process dramatically. We propose a novel adaptation of the Particle Swarm Optimization (PSO) algorithm, specifically tailored to the intricate multi-objective challenges of AGV energy prediction. This framework harnesses the strengths of a multi-objective approach, enabling the simultaneous optimization of prediction accuracy and model complexity. To further enhance efficiency, we seamlessly integrate GPU parallelization techniques, significantly expediting both the optimization process and the training of Artificial Neural Networks (ANNs) employed for prediction. Preliminary results demonstrate a remarkable improvement in the accuracy of AGV energy consumption predictions, directly attributed to the synergistic effect of optimizing the ANN architecture and parameters through our proposed PSO framework. This tailored PSO adaptation distinguishes itself by its ability to tackle the complex multi-objective nature of AGV energy prediction with enhanced efficiency and precision. It thus emerges as a compelling and novel solution within the realm of Machine Learning and heuristic methods for agricultural robotics, paving the way for sustainable and optimal AGV operations.

Enhancing efficiency in closed agricultural greenhouses: A data-driven predictive model for energy consumption

Abstract Predicting energy consumption in agricultural greenhouses is essential to effectively allocate resources, enhance plant growth, and minimize energy inefficiencies. Various factors affect the energy consumption inside the greenhouse including external climate conditions and internal microclimate. Proper understanding of these factors is crucial for maintaining an ideal growing environment and optimizing energy efficiency. This drives the need to investigate the interaction between these factors and greenhouse energy consumption, encompassing the energy needed for cooling and the supply of water and nutrients. This work aims at developing a dynamic model that predicts the total energy consumption of a closed agricultural greenhouse to improve microclimate control and energy efficiency. The study is conducted within a closed-loop agricultural greenhouse with no natural ventilation. Inside, the air is cooled and continuously circulated without being exchanged with ambient air through a heating, ventilation, and air conditioning (HVAC) system. The data-driven model encompasses external climate parameters such solar radiation, ambient temperature, and relative humidity; along with microclimate parameters such as internal temperature, humidity, and CO2 concentration to predict overall energy consumption. The study examines two machine learning models, deep neural networks (DNN) and extreme gradient boosting (XGBoost), for forecasting energy consumption, and assesses their performance using the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute error (MAE). Results reveal that the DNN model surpasses the XGBoost model, exhibiting a superior predictive performance with an R2 of 80.9%, RMSE of 171.1 kWh and MAE of 130.3 kWh. This study demonstrates its practicality in assisting with energy consumption analyses and identifying inefficient energy usage patterns within closed agricultural greenhouses.

A general sizing methodology of grid-connected PV systems to meet the zero-energy goal in buildings

This work expresses the most general definition of zero-energy building through a set of equations based on measured energy consumption and electricity generation predictions. This helps determining the optimal size of grid-connected photovoltaic systems for upgrading any highly efficient or conventional building into a zero-energy building. Opposed to previous works, which consider only electrical consumptions, this article includes consumption from fossil fuels and district heat and cooling networks. The study also covers environmental parameters and an economic study adaptable to any electricity market structure, contributing to deciding the best electricity tariff to hire.The methodology was applied to passive and conventional single-family houses in Spain. Results showed that the performance of a grid-connected photovoltaic system depends critically on the energy sources imported by the building. In particular, the performance improves in buildings where the electricity represents most of the imported energy. The required minimum size of the photovoltaic system in the conventional house (14 kWp) is far greater than in the passive house (9 kWp). After the recent global increase in electricity prices, annual economic savings of 70.12 % and 49.71 %, and payback periods of 6 and 13 years for the passive and conventional houses, respectively, make the investment profitable.

Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings

The management of large enterprises influences their efficiency and profitability. One of the important aspects is the appropriate management of electricity consumption used for production and daily operation. The problem becomes more complicated when you need to manage not one but a large complex of buildings with heterogeneous purposes. In the paper, we examine real-time series data of electrical energy consumption in a complex of heterogeneous buildings, including offices and warehouses, using time series analysis methods such as the Holt–Winters model and ARIMA/SARIMA model, and neural networks (Deep Neural Network, Recurrent Neural Network, and Long Short-Term Memory). Experimental research was performed on a dataset obtained from an energy consumption meter placed in the building complex, built in different periods, and equipped with a variety of automation devices. The data were collected over a period of four years 2018–2021 in the form of time series. Results show that classic models are good at predicting energy consumption in the mentioned type of buildings. The ARIMA model gave the best results—for buildings characterized by seasonality and trends the forecasts were almost perfect with actual values.

Machine learning algorithms for energy efficiency: Mitigating carbon dioxide emissions and optimizing costs in a hospital infrastructure

Hospital buildings incur significant energy consumption due to their expensive operations, advanced medical equipment, strict sanitation protocols, and compliance with environmental standards in various weather conditions. For this study, focusing on two distinct climate regions in Turkey—Aksaray and Bursa provinces—a total of 1,440 unique scenarios were generated. These scenarios utilized materials from the Revit Building Information Modeling (BIM) software library, incorporating variations in thermal transmittance coefficients, solar heat gain coefficients, and building orientation angles. The research employed machine learning algorithms to predict energy consumption, carbon dioxide (CO2) emissions, total expenditures, and life cycle costs. While the Revit BIM software library provides numerous combinations of building materials, it’s important to note that only a limited subset undergoes practical testing during construction. The main objective is to streamline the estimation of energy consumption, CO2 emissions, overall costs, and life cycle expenditures in similar architectural settings and under comparable climate conditions, eliminating the need for drafting and energy calculation software. Across all machine learning algorithms, predictions generated by artificial neural networks closely aligned with actual values. Notably, the R-squared (R2) values, a critical evaluation metric, produced the following outcomes: both R2 values for energy were 0.95; total costs achieved 0.93 and 0.97; CO2 emissions attained 0.94 and 0.97; and life cycle costs garnered 0.95 and 0.94 for validation and test datasets, respectively. These results suggest that the successes observed in the two provinces can be extrapolated to data from all regions in Turkey. Moreover, our model is capable of predicting energy consumption, costs, and CO2 emissions based on thermal transmittance values complying with the Turkish standard TS 825 and building orientation angles.

Federated Learning-Based Prediction of Energy Consumption from Blockchain-Based Black Box Data for Electric Vehicles

In modern society, the proliferation of electric vehicles (EVs) is continuously increasing, presenting new challenges that necessitate integration with smart grids. The operational data from electric vehicles are voluminous, and the secure storage and management of these data are crucial for the efficient operation of the power grid. This paper proposes a novel system that utilizes blockchain technology to securely store and manage the black box data of electric vehicles. By leveraging the core characteristics of blockchain—immutability and transparency—the system records the operational data of electric vehicles and uses federated learning (FL) to predict their energy consumption based on these data. This approach allows the balanced management of the power grid’s load, optimization of energy supply, and maintenance of grid stability while reducing costs. Additionally, the paper implements a searchable black box data storage system using a public blockchain, which offers cost efficiency and robust anonymity, thereby enhancing convenience for electric vehicle users and strengthening the stability of the power grid. This research presents an innovative approach to the integration of electric vehicles and smart grids, exploring ways to enhance the stability and energy efficiency of the power grid. The proposed system has been validated through real data and simulations, demonstrating its effectiveness and performance in managing black box data and predicting energy consumption, thereby improving the efficiency and stability of the power grid. This system is expected to empower electric vehicle users with data ownership and provide power suppliers with more accurate energy demand predictions, promoting sustainable energy consumption and efficient power grid operations.

A paradigm shift in solar energy forecasting: A novel two-phase model for monthly residential consumption

Accurately predicting residential solar energy consumption is crucial for efficient electricity production, supply, and power dispatch. However, conventional forecasting methods often struggle to handle complex energy consumption data. In response to this challenge, this study develops a pioneering two-stage error-corrected combined forecasting model that integrates traditional linear methods, seasonal processing techniques, deep learning models, and intelligent optimization algorithms to outperform other combined forecasting methods in terms of performance. This research analyzes the combined weight values, shedding light on why the proposed model consistently outperforms its counterparts. To confirm its superiority, the proposed model and five benchmark models are rigorously tested in this paper using four evaluation metrics and a hypothesis testing method. The empirical results show that the proposed combined model performs well in terms of accuracy and stability. Notably, the average absolute percentage error of its 24-step ahead prediction is 2.9053 %, which outperforms all comparative models, both single and combined model. These results fully illustrate the advantages of the combined model and reaffirm the excellence of its prediction performance in predicting energy consumption.

Energy-Aware Hierarchical Reinforcement Learning Based on the Predictive Energy Consumption Algorithm for Search and Rescue Aerial Robots in Unknown Environments

Aerial robots (drones) offer critical advantages in missions where human participation is impeded due to hazardous conditions. Among these, search and rescue missions in disaster-stricken areas are particularly challenging due to the dynamic and unpredictable nature of the environment, often compounded by the lack of reliable environmental models and limited ground system communication. In such scenarios, autonomous aerial robots’ operation becomes essential. This paper introduces a novel hierarchical reinforcement learning-based algorithm to address the critical limitation of the aerial robot’s battery life. Central to our approach is the integration of a long short-term memory (LSTM) model, designed for precise battery consumption prediction. This model is incorporated into our HRL framework, empowering a high-level controller to set feasible and energy-efficient goals for a low-level controller. By optimizing battery usage, our algorithm enhances the aerial robot’s ability to deliver rescue packs to multiple survivors without the frequent need for recharging. Furthermore, we augment our HRL approach with hindsight experience replay at the low level to improve its sample efficiency.

Leveraging explainable AI for informed building retrofit decisions: Insights from a survey

Accurate predictions of building energy consumption are essential for reducing the energy performance gap. While data-driven energy quantification methods based on machine learning deliver promising results, the lack of Explainability prevents their widespread application. To overcome this, Explainable Artificial Intelligence (XAI) was introduced. However, to this point, no research has examined how effective these explanations are concerning decision-makers, i.e., property owners. To address this, we implement three transparent models (Linear Regression, Decision Tree, QLattice) and apply four XAI methods (Partial Dependency Plots, Accumulated Local Effects, Local Interpretable Model-Agnostic Explanations, Shapley Additive Explanations) to an Artificial Neural Network using a real-world dataset of 25,000 residential buildings. We evaluate their Prediction Accuracy and Explainability through a survey with 137 participants considering the human-centered dimensions of explanation satisfaction and perceived fidelity. The results quantify the Explainability-Accuracy trade-off in building energy consumption forecasting and how it can be counteracted by choosing the right XAI method to foster informed retrofit decisions. For research, we set the foundation for further increasing the Explainability of data-driven energy quantification methods and their human-centered evaluation. For practice, we encourage using XAI to reduce the acceptance gap of data-driven methods, whereby the XAI method should be selected carefully, as the Explainability within the methods varies by up to 10 %.