Energy Consumption Prediction Method Research Articles

A Comparative Study of Building Energy Consumption Prediction Methods

Continued usage of fossil fuel-based electricity generation is one of the main factors for the greenhouse effect. As the greenhouse effect increase, the ambient temperature of the earth will also increase. As the temperature increase, the usage of air conditioning in buildings is also expected to increase. Since that more than 50 % of building energy consumption goes to air conditioning, it is utterly important to look into the methods that can be used to predict the impact of variations in temperature towards building energy consumption. This paper hence tries to perform a conceptual review on the prevalent methods that have been used for prediction of energy consumption, grouped in the category known as the white box, grey box and black box. The comparison of the different methods used in forecasting of energy consumption is discussed in terms of application, input, specific approach, advantage and disadvantage. It is hoped that the work carried out in this paper will be the reference to jump start the research work of others in this field of study.

A short-term energy consumption forecasting method for attention mechanisms based on spatio-temporal deep learning

Short-term energy consumption forecasting is the foundation of anomaly detection, scheduling and energy-saving control in manufacturing system. Existing methods do not reasonably distinguish between energy consumption nodes, but rather adopt overall prediction, resulting in poor prediction of target nodes. Consequently, this paper proposes a short-term energy consumption prediction method for the Attention Mechanism based on spatio-temporal deep learning (ST-Attention). Convolutional Neural Network (CNN) is utilized to extract feature extraction to acquire local high-dimensional features; Local long-term trend features are extracted using Gate Recurrent Unit (GRU) to reduce the inaccuracy of prediction due to the mixing of raw data;Finally, the Attention Mechanism is utilized to dynamically assign the weight of energy consumption nodes attribute to effectively deals with the difference, anisotropy and correlation between energy consumption nodes. The attention mechanism fuses extracted temporal and spatial features through weight assignment to enable short-term energy consumption prediction for manufacturing systems. The actual data collected from the aluminum profile factory is used to verify that the ST-Attention model providing better performance for short-term energy consumption prediction of the manufacturing system.

Prediction of Energy Consumption in Horizontal Roughing Process of Hot Rolling Strip Based on TDADE Algorithm

The steel industry is the key industry of energy consumption. The optimization of rolling process parameters is an effective measure to reduce and optimize energy consumption. Accurate energy consumption prediction has an indispensable guiding function in the planning of process parameters. However, the traditional energy consumption prediction and machine learning methods have been unable to fit the needs of high precision and reliability. Taking the horizontal roughing process (HRP) of hot rolling process as the research object, this paper mainly introduces an energy consumption prediction mechanism model (ECPMM) based on a roller adaptive wear strategy by analyzing the strip forming mechanism and rolling process. Then, two directions adaptive differential evolution (TDADE) algorithm is proposed based on a novel adaptive strategy. This algorithm has better convergence speed and global optimization ability than other optimization algorithms in the parameter optimization of ECPMM. We carried out comparative experiments on five machine learning algorithms. The results show that the ECPMM optimized by TDADE is superior to the five machine learning algorithms in prediction accuracy and stability. Finally, we conducted ablation experiments on the energy consumption characteristics of the HRP process, which turns out that using a smaller reduction and roller radius can reduce energy consumption. Summarizing the above experimental results suggests that the ECPMM has high accuracy and robustness, and satisfies the requirements of practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —It is necessary to carry out the deep analysis and research of energy consumption prediction involved in the process planning of strip hot rolling, the inspiration of this article is stem from this point. The commonly used energy consumption prediction methods include mechanism modeling and machine learning, but both have disadvantages, like low prediction accuracy and black box, which must have to be performed to solve by adapting the production demand of high reliability. In this paper, the combination of the mechanism model and data-driven method is used to realize the accurate prediction of energy consumption and improve the reliability of the model. To the best of our knowledge, this is the first paper about the research on energy consumption prediction of hot rolling process based on mechanism model and data-driven, especially in data size, time granularity, algorithm design, and prediction performance. Such research is helpful to guide engineers to design rolling energy consumption. We will dive a bit deeper into learning the energy consumption and process parameters during the rolling process in future studies.

Building energy consumption prediction method based on Bayesian regression and thermal inertia correction

The accurate prediction of building energy consumption is a crucial prerequisite for demand response (DR) and energy efficiency management of buildings. Nevertheless, the thermal inertia and probability distribution characteristics of energy consumption are frequently ignored by traditional prediction methods. This paper proposes a building energy consumption prediction method based on Bayesian regression and thermal inertia correction. The thermal inertia correction model is established by introducing an equivalent temperature variable to characterize the influence of thermal inertia on temperature. The equivalent temperature is described as a linear function of the actual temperature, and the key parameters of the function are optimized through genetic algorithm (GA). Using historical energy usage, temperature, and date type as inputs and future building energy comsuption as output, a Bayesian regression prediction model is established. Through Bayesian inference, combined with prior information on building energy usage data, the posterior probability distribution of building energy usage is inferred, thereby achieving accurate forecast of building energy consumption. The case study is conducted using energy consumption data from a commercial building in Nanjing. The results of the case study indicate that the proposed thermal inertia correction method is effective in narrowing the distribution of temperature data from a range of 24.5°C to 36.5°C to a more concentrated range of 26.5°C to 34°C, thereby facilitating a more focused and advantageous data distribution for predictions. Upon applying the thermal inertia correction method, the relative errors of the Radial Basis Function (RBF) and Deep Belief Network (DBN) decreases by 2.0% and 3.1% respectively, reaching 10.9% and 7.0% correspondingly. Moreover, with the utilization of Bayesian regression, the relative error further decreases to 4.4%. Notably, the Bayesian regression method not only achieves reduced errors but also provides probability distribution, demonstrating superiority over traditional methods.

A data-driven energy consumption prediction method for building electrical equipment based on data-driven

A data-driven energy consumption prediction method for building electrical equipment based on data-driven

Predictive model of pump energy consumption based on image feature extraction and instance-based transfer learning

The energy consumption of pumps for water supply accounts for a high proportion of the total global energy consumption. The analysis of the pump's energy consumption factors and the construction of prediction models play a crucial role in carbon emission reductions throughout the water supply process. However, difficulties in accurately constructing input features under complex variables and the scarcity and anomalies of monitoring data during system operation pose great challenges to accurate energy consumption prediction. In order to address this issue, a water pump energy consumption prediction method based on image feature extraction and instance transfer is proposed, using the water pump systems installed in two residential communities as the research objects. Firstly, a novel image-based correlation determination method for feature engineering was introduced and validated on a sufficient dataset. Subsequently, in the context of poor data transfer learning, a water pump energy consumption prediction model, named Two-stage TrAdaBoost.R2 (TSTR), was established by incorporating the image correlation determination method and fine-tuning the model parameters using the particle swarm optimization algorithm to enhance prediction accuracy. The experimental results demonstrated the effectiveness of the proposed image-based correlation determination method, showing a 5.49%–44.33% improvement in the coefficient of variation of root mean square error (CV-RMSE) prediction accuracy compared to classical time-series correlation determination methods. Furthermore, in the poor data transfer prediction experiment, the TSTR-based model achieved 0.90% and 20.22% higher CV-RMSE prediction accuracy compared to two comparative models. These findings confirmed the effectiveness of instance transfer learning methods in poor data scenarios and provided valuable insights for selecting input feature construction methods under complex variables.

Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network

With increasing energy consumption, how to achieve the energy-saving operation of air-conditioning systems is crucial for improving the energy efficiency of buildings. The accurate and reliable energy consumption prediction of air-conditioning systems can be useful for optimizing the energy supply and equipment operation strategies. However, most existing studies focus on the prediction of the long-term energy consumption of air-conditioning systems, which usually exceeds the individual control time steps of the air-conditioning system. If the energy consumption of air-conditioning systems in the next control step can be predicted, then the least energy-consuming operating strategy can be selected at the beginning of each control step by relying on the predicted energy consumption, thereby achieving greater energy conservation. This paper proposes a real-time energy consumption prediction model for air-conditioning systems based on a long short-term memory neural network. The correlation between the energy consumption of air-conditioning system and input variables can be obtained by calculating the Spearman correlation coefficient. The predicted model results show that the normalized root mean square error of the prediction model is 0.0429 when the indoor air temperature, condenser inlet temperature, and historical energy consumption are set as inputs to predict the energy consumption of the air-conditioning system for the next 15 min. Meanwhile, the energy consumption at a certain indoor temperature setpoint can be predicted by the prediction model to aid in selecting a more appropriate indoor temperature setpoint, which can reduce energy consumption by optimizing the operation strategy of the air-conditioning system at each control step.

An energy consumption prediction method for HVAC systems using energy storage based on time series shifting and deep learning

The prediction of building energy consumption plays a crucial role in responding to energy demands and achieving low-carbon control through energy saving. In this study, we focused on peak shaving and valley filling in the energy consumption of office building energy-storage HVAC systems. A time series shifting method based on the Pearson correlation coefficient was combined with a gate recurrent unit (GRU) deep learning model to predict HVAC energy consumption. Additionally, we explored the adaptability of this method to non-deep learning support vector regression (SVR) and random forest (RF) models. The results demonstrated that the Kaiser-Meyer-Olkin test values of HVAC energy consumption, meteorological parameters, and indoor environmental parameters improved by 10.8%. The GRU model exhibited a 12% reduction in the root mean square error (RMSE), and the coefficient of determination (R2) was 0.850. The RMSE of the RF model decreased to 12.24 kW·h, with a 12% increase in R2 to 0.760. The SVR model achieved an RMSE of 12.59 kW·h and an R2 of 0.793. Therefore, this method can effectively improve the prediction accuracy of models by enhancing the correlation between their input and output variables.

Development of a hybrid VRF system energy consumption prediction model based on data partitioning and swarm intelligence algorithm

Accurately forecasting energy consumption is beneficial and pivotal for effectively managing variable refrigerant flow (VRF) systems. Changes in energy consumption provide an intuitive representation of the operating condition and the impact of possible faults. Energy consumption prediction is fundamental in energy conservation tasks such as fault diagnosis. Currently, many energy consumption prediction model have been applied for HVAC system widely. However black-box energy prediction models applied to VRF systems are still relatively few and crude. This study therefore proposed a hybrid energy consumption prediction method for VRF systems based on data partitioning and swarm intelligence algorithm. The model was based on back propagation neural network (BPNN), and utilized data partitioning techniques to identify the energy consumption patterns of the VRF system. For each pattern, a BPNN sub-model was trained using operating and energy consumption data of a VRF system. Then, swarm intelligence algorithm is adopted to determine the optimal architecture of each BPNN sub-model. The results demonstrate that the proposed hybrid models can achieve better prediction performance than single models like BP Neural Network. Root mean square error and mean absolute percentage error of the proposed predictive model is 202.49 and 1.95%, which has a 31.3% and 45.5% decrease compared to single BPNN model. At the same time, it is enlightening for other researchers to study the potential of data partitioning algorithm and swarm intelligence algorithm in the field of VRF system energy consumption prediction.

Short-term energy consumption prediction method for educational buildings based on model integration

Paying attention to the feature engineering problems is the basis for constructing a more accurate building energy consumption prediction model, which helps debug, control, and operate building energy management systems. Therefore, in this paper, an integrated energy consumption prediction model considering spatial characteristics in time series data is proposed to predict the short-term energy consumption of educational buildings, and the influence of features on the model is analyzed using the cooperative game theory SHAP method, and the optimal number of features is determined by ablation analysis. The proposed model is validated by an educational building in Xi'an, Shaanxi Province. The results show that compared with other energy consumption prediction models, the RMSE value of the integrated energy consumption prediction model is reduced by 13.64%–34.55%, and the MAE value is reduced by 10.25%–30.54%, which has higher prediction accuracy. In addition, this paper also investigates the minimum amount of data and the number of features required for the training of the building energy prediction model, and the integrated energy prediction model can still effectively predict building energy consumption when the training samples are minimal and the number of features is appropriate.

Predication of smart building energy consumption based on deep learning algorithm

&lt;p&gt;Since smart cities have received extensive attention in recent years, and there is no more research data on energy consumption in smart cities. In order to improve the energy consumption prediction accuracy of intelligent buildings, a building energy consumption prediction method based on deep learning algorithm is proposed. By predicting the power consumption, we can analyze whether the energy consumption of the building is reasonable, so as to make further management actions. First of all, the specifies the overall data processing system by using the method of cloud computing, and the overall data is stored and calculated by means of cloud computing. In order to verify the effectiveness of the algorithm in this paper, the algorithm in this paper is applied to commercial buildings, and the data is compared with other algorithms. The results show that, whether compared with the data regression model or with other learning methods, the algorithm in this paper has obvious advantages in prediction accuracy and stability, and can be used to predict the energy consumption of buildings.&lt;/p&gt;

Building energy consumption prediction based on VMD-CNN-BILSTM-Attention

Accurate building energy consumption prediction can help buildings optimize energy allocation and save energy. To achieve accurate and reliable building energy consumption prediction, this paper proposes a building energy consumption prediction method based on VMD decomposition and noise reduction, i.e., CNN-BILSTM-Attention hybrid prediction. In this paper, we first use the VMD method to decompose the historical energy consumption series to obtain relatively smooth multiple components, which lays the foundation for accurate prediction. Then we combine Convolutional Neural Network (CNN) and Bi-directional Long and Short Term Memory (BILSTM) network to fully extract the Spatio-temporal characteristics of energy consumption data and then introduce the Attention mechanism to automatically assign corresponding weights to the BILSTM hidden layer states to distinguish the importance of different influencing factors on energy consumption and improve the prediction accuracy. To verify the effectiveness of the model, the most later analyzed with real building energy consumption data of a region, and the prediction results were shown by python, and the prediction results were improved by 40% compared with the traditional machine learning algorithm.

Short-term energy consumption prediction of electric vehicle charging station using attentional feature engineering and multi-sequence stacked Gated Recurrent Unit

Energy consumption prediction is critical to intelligent power dispatching and smart grid optimization. However, the task remains challenging due to big data fluctuation and the low accuracy of a single feature. This paper proposes a novel short-term energy consumption prediction method for electric vehicle charging stations based on attention feature engineering and stacked GRU (Gated Recurrent Unit). First, we select several handcrafted features from historical energy consumption data. Second, we assign weights to these handcrafted features using the attention mechanism. Third, we propose a multi-sequence stacked GRU in parallel architecture as the network core to extract the correlations between the base and handcraft features. We mitigate the data fluctuations by modifying the prediction resolution and evaluate our method by comparing it with RNN, stacked-GRU, and DeepDeff GRU. The experiment results demonstrate that our method outperforms the state-of-the-art method with a prediction accuracy improvement of 27.2%.

A novel energy consumption prediction method for chillers based on an improved support vector machine

The energy consumption prediction of the chiller is an important means to reduce the energy consumption of buildings. Therefore, a novel energy consumption prediction model for chillers based on an improved support vector machine (ICA-DE-SVM) is proposed. The imperialist competitive algorithm (ICA) is used to optimize the penalty coefficient and kernel function width of SVM, greatly improving the generalization ability and prediction accuracy of the SVM model. The assimilation process is very important in ICA. Colonies of empires move randomly toward imperialists during the assimilation process in ICA, which decreases population diversity and can lead to premature convergence. Therefore, to create more new locations for colonies and increase population diversity, the idea of differential mutation proposed by differential evolution (DE) was applied to ICA. The established model was experimentally verified in an actual multi-chiller system in a building, and the results showed that the ICA-DE-SVM model could obtain good prediction results. Finally, the proposed model was compared with SVM model, PSO-SVM model, GA-SVM model, WOA-SVM model, and ICA-SVM model. With an MAPE of 0.6%, an MSE of 2.3, and an R2 of 0.9998, the findings demonstrate that the ICA-DE-SVM model has a greater prediction accuracy than the other models.

An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

Large and extensive manufacturing systems consume a large proportion of manufacturing energy. A key component of energy efficiency management is the accurate prediction of energy efficiency. However, the nonlinear and vibration characteristics of machining systems’ energy consumption (EC) pose a challenge to the accurate prediction of system EC. To address this challenge, an energy consumption prediction method for machining systems is presented, which is based on an improved particle swarm optimization (IPSO) algorithm to optimize long short-term memory (LSTM) neural networks. The proposed method optimizes the LSTM hyperparameters by improving the particle swarm algorithm with dynamic inertia weights (DIWPSO-LSTM), which enhances the prediction accuracy and efficiency of the model. In the experimental results, we compared several improved optimization algorithms, and the proposed method has a performance improvement of more than 30% in mean absolute error (MAE)and mean error(ME).

Pump Feature Construction and Electrical Energy Consumption Prediction Based on Feature Engineering and LightGBM Algorithm

In recent years, research on improving the energy consumption ratio of pumping equipment through control algorithms has improved. However, the actual behavior of pump equipment and pump characteristic information do not always correspond, resulting in deviations between the calculated energy consumption operating point and the actual operating point. This eventually results in wasted power. To solve this problem, the data from circulating pumping equipment in a large pumping facility are analyzed, and the necessary characteristics of pumping equipment electrical energy consumption are analyzed through a subset of mechanism expansion feature engineering using the Pearson correlation coefficient algorithm. Based on this, a pump energy consumption prediction method based on LightGBM is constructed and compared with other algorithm models. To improve the generalization ability of the model, rules applicable to pump power energy consumption prediction are proposed, and the model features and processes are reduced. Based on the mechanistic model, 18 features related to electric energy consumption are selected, and 6 necessary features of pump electric energy consumption are screened by feature engineering. The experimental results show that the LightGBM regression algorithm has a significant prediction effect with R2=0.94. After the importance analysis, three features that are strongly related to pump energy consumption are finally screened out. According to the prediction results, the feature engineering dataset was selected and the pump electrical energy consumption was predicted based on the LightGBM algorithm, which can significantly reduce the problem of deviation in the prediction of the electrical energy consumption of pumping equipment.

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate.

Prediction and Analysis of Energy Consumption in Fused Deposition Processing Based on G-code

In order to solve the problem of energy consumption prediction in the fused deposition process, an energy consumption prediction method is proposed by associating G-code files with fused deposition hardware. This method systematically studies the energy consumption in the process of fused deposition 3D printing by analyzing the relationship between G code files and fused deposition 3D printers. Starting from the energy consumption mechanism, a prediction model of fused deposition energy consumption for G code files is proposed. By obtaining the specific parameters of the fused deposition processing equipment and the selected experimental model, the experimental process parameter level is designed, and the orthogonal experiment is carried out to predict the energy consumption of the fusion processing in detail. The verification shows that the method can not only predict an accuracy rate of more than 90%, but also effectively predict the optimal process parameter value, which fully proves that the prediction method has high prediction accuracy and good practicability, and can be used for model processing. Provide tool support for energy consumption prediction before processing, saving production costs.

IECL: An Intelligent Energy Consumption Model for Cloud Manufacturing

The high computational capability provided by a data center makes it possible to solve complex manufacturing issues and carry out large-scale collaborative cloud manufacturing. Accurately, real-time estimation of the power required by a data center can help resource providers predict the total power consumption and improve resource utilization. To enhance the accuracy of server power models, we propose a real-time energy consumption prediction method called IECL that combines the support vector machine, random forest, and grid search algorithms. The random forest algorithm is used to screen the input parameters of the model, while the grid search method is used to optimize the hyperparameters. The error confidence interval is also leveraged to describe the uncertainty in the energy consumption by the server. Our experimental results suggest that the average absolute error for different workloads is less than 1.4% with benchmark models.

Prediction Method for Office Building Energy Consumption Based on an Agent-Based Model Considering Occupant–Equipment Interaction Behavior

Traditional building energy consumption prediction methods lack the description of occupant behaviors. The interactions between occupants and equipment have great influence on building energy consumption, which cause a large deviation between the predicted results and the actual situation. To address this problem, a two-part prediction model, consisting of a basic part related to the building area and a variable part related to stochastic occupant behaviors, is proposed in this study. The wavelet decomposition and reconstruction method is firstly used to split the energy consumption. A relationship between the low frequency energy consumption data and the building area is discovered, and an area-based index is used to fit the basic part of the prediction model. With a quantitative description of the occupant–equipment interaction by classifying the equipment into environmentally relevant and environmentally irrelevant equipment, an agent-based model is established in the variable part. According to the validation given by two case office buildings, the prediction error can be controlled to 2.8% and 10.1%, respectively, for the total and the hourly building energy consumption. Compared to the prediction method which does not consider occupant–equipment interactions, the proposed model can improve prediction accuracy by 55.8%.