Energy Consumption Prediction Model Research Articles

Energy consumption forecast model of CNC machine tools based on support vector regression optimized by improved artificial hummingbird algorithm

With the development of the manufacturing industry, energy consumption is growing rapidly, which makes the energy crisis and environmental problems become more and more serious. CNC machine tools play an essential role and are the primary energy consumption devices in the manufacturing industry. The accurate prediction of machine tool energy consumption can provide support for energy production plans and reduce energy waste. This paper proposes a novel energy consumption prediction model based on support vector regression (SVR) optimized by an improved artificial hummingbird algorithm (IAHA). Firstly, as the artificial hummingbird algorithm (AHA) may easily get trapped in a local optimum, an improved AHA based on chaotic mapping and local backtracking exploitation strategy is proposed. The chaotic mapping is used to initialize individual positions, which is good for maintaining population diversity. The local backtracking exploitation strategy is employed to improve the local optimization ability. The effectiveness and feasibility of the IAHA algorithm have been verified through 12 benchmark functions. Then, the IAHA algorithm is employed to optimize the parameters of the SVR, and the IAHA-SVR energy consumption prediction model is established. Finally, the effectiveness and feasibility of the IAHA-SVR model are verified through a case study.

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building’s energy consumption.

Research on the Energy Consumption Influence Mechanism and Prediction for the Early Design Stage of University Public Teaching Buildings in Beijing

The public teaching buildings of universities have a large flow of people, high lighting requirements, and large energy consumption, which present significant potential for energy saving. The greatest opportunity for integrating “green” architectural design strategies lies in the design phase, especially the early stage of architectural design. However, current designers often rely on experience or qualitative judgment for decision-making. Thus, there is a pressing need for rational and quantitative green architectural design theories and techniques to guide and support decision-making for the design parameters of teaching buildings. This study, based on field surveys of 40 teaching buildings, constructs building archetypes regarding energy consumption including 28 typical values. Based on the “Rectangle”, “L”, “U”, and “Courtyard” archetypes, through batch energy consumption simulation and multiple regression methods, the influence mechanisms of nine energy consumption influencing factors on four types of building energy consumptions were explored, and energy consumption prediction models were derived. The findings of this research can serve as factor evaluation and selection in the early stage of architectural design for public teaching buildings at universities, and the prediction model can assist in the early estimation of energy consumption. This aims to enrich and supplement green architectural design methods by supporting the design of green public teaching buildings and providing reference and application for relevant engineering practices.

Modeling and predicting energy consumption of chiller based on dynamic spatial-temporal graph neural network

As an important equipment in the HVAC system, the chiller accounts for 50-60% of the total energy consumption. According to the characteristics of chiller operation data and using the multivariate time series (MTS) forecasting method, a novel chiller energy consumption prediction model (T-CADGCN) is proposed, which is based on the temporal convolution (TC) and the dynamic graph convolution (DGC) network with a channel attention (CA) mechanism. Two datasets are used to verify the prediction performance of different models. Compared with the actual energy consumption, the average MAPEs of the T-CADGCN model on the measured dataset and simulated dataset are 0.92% and 0.08%, respectively. Whereas they are 2.8% and 0.91% for LSTM, 11.45% and 11.74% for MTGNN_GCA, 12.3% and 12.63% for MTGNN, respectively. The results demonstrate that the T-CADGCN model can effectively enhance the prediction performance of chiller energy consumption, followed by LSTM, then MTGNN_GCA, and finally MTGNN. Besides, the prediction performance on different future time steps is also conducted on two datasets. It can be found that all models decrease in prediction accuracy with the increase of the prediction time steps, but the T-CADGCN model can maintain a relatively good prediction performance with high robustness. The changes of in-degree and out-degree in the chiller energy consumption node in the dynamic graph construction module indicate that the factors affecting and being affected by chiller energy consumption change over time. This study can help to predict the energy consumption of chiller accurately and thus contributes to building energy management.

Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack operational data due to relatively limited monitoring systems, causing the failure to use data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed model CV-RMSE improves 81.3% (AEC) and 77.4% (EEC), respectively, compared to the prediction model that does not implement the transfer learning strategy and directly uses the target BEC data. As for the short-term multi-step prediction, the proposed model CV-RMSE improves 62.0% (AEC) and 65.5% (EEC), respectively. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan.

Building energy efficiency assessment base on predict-center criterion under diversified conditions

With the rapid expanding of urban economy and population, how to reduce the energy consumption and improve energy efficiency of public buildings has become a key issue for energy conservation and urban development. However, due to the diversity of building usage condition, energy consumption data often has problems with nonlinearity, outliers, and diversity, which greatly reduce the accuracy of energy consumption prediction and energy conservation assessment. To overcome the above drawbacks, a regression prediction model of building energy consumption is designed, which cluster the same type of data characteristics to form a subset of equipment through Gaussian mixture clustering, and design the specific normalized adaptive loss function to determine the optimal control parameters of regression model. Furthermore, a novel predict-center energy efficiency evaluation criterion is proposed, and the cumulative distribution pattern is further statistically analyzed. Finally, the energy benchmark evaluation framework is proposed to determine the energy efficiency evaluation grade. Through comparative experimental analysis, it can be seen that the proposed prediction model can adapt to different types of building data, and have better prediction accuracy and convergence speed. The consistency and robustness of the benchmark evaluation framework and method are optimal.

Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models for Accurate Energy Consumption Prediction

Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks’ features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study’s innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future.

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately.

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error.

Energy Consumption Prediction Model for Smart Homes via Decentralized Federated Learning With LSTM

Energy Consumption Prediction Model for Smart Homes via Decentralized Federated Learning With LSTM

Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms

Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms

Performance assessment of cross office building energy prediction in the same region using the domain adversarial transfer learning strategy

Precise and reliable building energy consumption prediction is of great significance for building energy management and future energy planning. To mitigate the adverse impact of data limitation on the performance of building energy consumption prediction model, a transfer learning strategy was introduced. However, the operation modes of different building energy systems are different, resulting in a large difference in the distribution of building energy consumption related data. In this paper, the relevant data of 25 buildings from the same region of the United States were selected. On this basis, two similarity calculation strategies are compared, and three similarity measurement methods were used to analyze the impact of different source buildings on the performance improvement of transfer learning models. The results show that choosing the appropriate source building can improve the transfer learning performance by up to 82.56% when compared to the no-transfer model (the baseline model). When higher similarity source buildings are selected, the corresponding average PIR is about 24.7% higher than the total average, which can make the transfer model performance improvement more obvious. This paper provides insights into the selection of source domains for transfer learning strategies under the same region.

A Novel Series-Concatenation Hybrid Prediction Model of Energy Consumption in Hot Strip Roughing Process With Multi-Step Rolling

A Novel Series-Concatenation Hybrid Prediction Model of Energy Consumption in Hot Strip Roughing Process With Multi-Step Rolling

Study on prediction and influencing factors of energy consumption in Jiangxi province

This study aims to determine the main factors affecting energy consumption in Jiangxi Province, and to forecast energy consumption effectively. An indicator system of energy consumption influencing factors containing 17 variables is constructed from 4 aspects of economy, society, technology and energy. Based on relevant data from 1996 to 2021, conduct comprehensive analysis and select 10 main factors. Establish three energy consumption prediction models: multiple regression model (MLR), time series model (ARIMA), and support vector regression (SVR). Evaluate the prediction performance through mean square error (MSE), mean absolute percentage error (MAPE), and goodness of fit (R2). The main influencing factors of energy consumption are determined as GDP, output value of the tertiary industry, industrial output value, residents’ consumption level, fixed assets investment of the whole society, total population, urbanization rate, internal expenditure of R&D funds, energy intensity, and energy consumption structure. The best prediction model is SVR model. It provides a basis for the effective formulation of energy policies and the implementation of economic development programs.

TCN-Attention-BIGRU: Building energy modelling based on attention mechanisms and temporal convolutional networks

<abstract> <p>Accurate and effective building energy consumption prediction is an important basis for carrying out energy-saving evaluation and the main basis for building energy-saving optimization design. However, due to the influence of environmental and human factors, energy consumption prediction is often inaccurate. Therefore, this paper presents a building energy consumption prediction model based on an attention mechanism, time convolutional neural (TCN) network fusion, and a bidirectional gated cycle unit (BIGRU). First, t-distributed stochastic neighbor embedding (T-SNE) was used to preprocess the data and extract the key features, and then a BIGRU was employed to acquire past and future data while capturing immediate connections. Then, to catch the long-term dependence, the dataset was partitioned into the TCN network, and the extended sequence was transformed into several short sequences. Consequently, the gradient explosion or vanishing problem is mitigated when the BIGRU handles lengthy sequences while reducing the spatial complexity. Second, the self-attention mechanism was introduced to enhance the model's capability to address data periodicity. The proposed model is superior to the other four models in accuracy, with an mean absolute error of 0.023, an mean-square error of 0.029, and an coefficient of determination of 0.979. Experimental results indicate that T-SNE can significantly improve the model performance, and the accuracy of predictions can be improved by the attention mechanism and the TCN network.</p> </abstract>

A scheduling algorithm based on critical factors for heterogeneous multicore processors

SummaryAs the development of chip manufacturing technology slows down, high‐performance processors often have high energy consumption and high heat generation. Therefore, heterogeneous multi‐core processors become more and more popular, and the heterogeneous multi‐core processors is adopted to execute programs. At present, the general program consists of multiple threads. To reach goals of accelerating program execution and reducing energy consumption and heat generation of system, a suitable thread scheduling algorithm for heterogeneous multi‐core processors is needed. In this article, a thread scheduling algorithm based on multiple critical scheduling factors is proposed. First, a prediction model of thread performance and energy consumption is used to predict the core sensitivity of threads. Then, critical threads are judged and accelerated by collecting the synchronization information between threads. Finally, the load balancing method based on the computing power of cores and the core sensitivity of threads is employed to perform system load balancing, which ensures the fairness of the scheduling. Several experiments are provided, and the results show that the proposed algorithm can obtain better performance of thread schedule.

Comparison of ethane recovery processes for lean gas based on a coupled model

Ethane recovery from natural gas is crucial for improving the economic efficiency of enterprises, saving energy and reducing emissions. This study analyses the characteristics of process parameters based on three lean gas ethane recovery processes. A prediction and multiobjective optimization model for ethane recovery and system energy consumption is established. Based on this framework, a new method for ethane recovery process comparison for lean gas is proposed. In the process analysis, two additional coolers are added to the cold residue recycling process to improve ethane recovery. Seven main process parameters are determined based on the three processes. According to the prediction results, support vector regression is the most effective model among the individual models. The prediction accuracy of the support vector regression model based on grey wolf optimization is the highest. Compared with traditional support vector regression, this approach yields average reductions in the mean absolute error, mean absolute percentage error and root mean square error of 63.95%, 64.21% and 47.98%, respectively, reflecting significantly improved prediction accuracy and stability. The multiobjective multiverse optimization algorithm displays the best optimization performance and the highest solution diversity among the optimization results. Additionally, it yields high-quality solutions at the shortest run time. Based on the optimization results, the supplemental rectification with compression process yields the lowest system energy consumption of any method considered and meets the ethane recovery requirements real projects.

The impact of electromobility in public transport: An estimation of energy consumption using disaggregated data in Santiago, Chile

Electromobility in public transport has become a promising way to reduce environmental pollution. Several contributions have sought to estimate the energy consumption of buses in public transport. However, most of these efforts use measurements collected from controlled or simulated experiments, or that do not characterize the entire bus network. Unlike these studies, this article estimates the energy consumption of all the electric buses that circulate in the city of Santiago, Chile, during the studied period using full disaggregated GPS data and empirical measurements on some sensorized electric buses. The methodology considers a feature selection phase and the development of energy consumption prediction models using physics based and machine learning approaches. The performances of both models are compared with each other, and then, the best one is used to measure the impact of electromobility in the city. This analysis allows decision-makers to target investment by determining the buses with higher energy consumption savings in the face of budget constraints.

Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion. Methods: A total of 100 subjects aged 18-30years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2km/h, 3km/h, 4km/h, 5km/h, and 6km/h) and running (speed range: 7km/h, 8km/h, and 9km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland-Altman analysis. Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76METs, Bias = 0.02METs) and the best performance in Bland-Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66METs, Bias = 0.03METs) and running (RMSE = 0.90METs, Bias < 0.01METs) separately, as well as high accuracy in predicting EE at each single speed. Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.

CorrDQN-FS: A two-stage feature selection method for energy consumption prediction via deep reinforcement learning

Accurate energy consumption prediction is crucial for optimizing energy usage and achieving sustainable development goals, especially in the field of building engineering. However, constructing accurate prediction models faces challenges in feature selection, given the large pool of candidate features and their intricate relationships. To address this issue, we propose a novel two-stage feature selection method called CorrDQN-FS for energy consumption prediction. CorrDQN-FS leverages the Pearson correlation coefficient to rank features and eliminates features with obvious irrelevant coefficients. Subsequently, the method integrates deep reinforcement learning techniques to optimize the feature selection process, considering the complex nonlinear relationships among features. By combining correlation analysis and deep reinforcement learning, CorrDQN-FS aims to enhance the accuracy of energy consumption prediction models. To evaluate the effectiveness of CorrDQN-FS, we conduct experiments using real-world data. We compare the performance of CorrDQN-FS combined with different prediction methods, including DF-DDPG, DF-DQN, LSTM, RNN, MLR and DT, to access its ability to improve energy consumption prediction accuracy. Additionally, we compare CorrDQN-FS with other feature selection methods, including the Pearson correlation coefficient, Recursive Feature Elimination (RFE), Mutual Information, and Gradient Boosting Decision Trees (GBDT), to highlight its advantages in feature selection. The experimental results demonstrate that CorrDQN-FS outperforms other feature selection techniques, effectively enhancing the accuracy of energy consumption prediction. This advancement holds significant potential for optimizing energy management and furthering sustainable building engineering.