Dynamic energy consumption monitoring and scheduling for green buildings: A comprehensive approach

In order to improve the accuracy of the short-term prediction of building energy consumption, this study proposes a short-term prediction model of building energy consumption based on the CEEMDAN-BiLSTM method. In this study, the energy consumption data of an office building in 2019 are selected as a sample, and CEEMDAN is used to decompose the energy consumption data into multiple components, and the strong correlation components are selected and sent to the BiLSTM network. The final energy consumption prediction results are obtained by superimposing the prediction results of each sub-component, and five models are built simultaneously to compare the errors with the proposed models. The results showed that the weather type has a great influence on the accuracy of energy consumption prediction. When the weather fluctuates greatly, the prediction error of energy consumption by a single prediction model is large. When the weather suddenly changes, the EMD-LSTM model has a big error in the prediction of air conditioning energy consumption. After CEEMDAN decomposition of energy consumption data, more detailed components can be extracted, which makes the BiLSTM prediction algorithm more accurate. Compared with the CEEMDAN-LSTM model, the CEEMDAN-BiLSTM model reduces eRMSE, eMAPE, and eTIC by 4.1%, 9.441, and 1.3%, respectively. The proposed model can effectively improve the accuracy of short-term prediction of building energy consumption.

Currently, the carbon emissions of building energy consumption account for a significant portion of all carbon emissions. How to reduce carbon emissions to achieve carbon neutrality is an important current research direction. Therefore this research builds a predictive algorithm model for analyzing energy consumption data of meteorological buildings using DeST platform for energy saving and emission reduction to achieve carbon neutrality. The new model uses Internet of Things and cloud platform technology to build a simulation building platform, and uses the support vector machine algorithm in the analysis algorithm to vectorize building energy consumption data, which can achieve normalization processing of building energy consumption and meteorological data. By processing building energy consumption data, prediction of building energy consumption at the next moment can be achieved. The experimental results show that the precision and accuracy of the new algorithm are higher than genetic algorithm 1 and 0.15 respectively, and 0.6 and 0.07 higher than clustering analysis algorithm respectively. Therefore, applying this algorithm model to building energy consumption prediction can significantly improve the accuracy and precision of the algorithm.

With the rapid development of economy, energy demand is increasing in Hebei. Therefore, prediction of energy consumption and structure in Hebei province has importance of actual meaning significance. In this paper, total energy, coal, oil and natural gas consumption data are selected in Hebei province between 2001 and 2013. First, energy consumption and structure in Hebei province are analyzed. Second, GM(1,1)forecast model is established. Then, according to the established forecast model, energy consumption and structure between 2014 and 2021 in Hebei province is predicted. Last, related suggestions on energy optimization are put forward. The results are expected to provide important scientific basis for energy utilization and planning in Hebei province. Introduction Grey prediction is a method that can predict the systems containing uncertainties. To find the laws of system changes, original data is generating processed by identifying development trend of dissimilarity degree between system factors. Thus, data sequence with high regularity is generated. And then the corresponding differential equation model is established to predict future development trend of things. GM (1,1) prediction model with a variable and first-order differential is an important model of grey prediction. It is commonly used in energy and environment prediction because this model requires less modeling information, operates easily, forecasts precisely and is easy to test. In this paper, total energy, coal, oil and natural gas consumption data in Hebei province between 2001 and 2013 are selected as original sequence. GM (1, 1) model is constructed to predict energy consumption and structure in following 20 years in Hebei province. It hopes to provide reference and scientific basis for energy development strategy and the establishment of energy planning in Hebei. Analysis of energy consumption and structure in Hebei province The energy data in Hebei province between 2000 and 2012 are from China energy statistical yearbook. In this paper, all the energy consumption data have been converted into standard coal and the unit is ten thousand tons of standard coal. Table one shows the energy consumption and consumption structure in Hebei province. As shown in table 1, the total energy consumption in Hebei province seems to be increasing annually from 2000 to 2012 and its average annual growth rate is 7.95%. However, the speed of total energy consumption growth is different during the period and it has periodic growth characteristic. From the table, we can see that the growth speed is rapid from 2001 to 2007. Energy consumption structure in Hebei province is basically stable in recent years because of restriction on resources endowment and consumption structure of energy relying mainly on coal cannot be changed. Coal accounts for about 90 percent in energy consumption before 2011, but oil, gas and electricity such clean energy consumption occupies 10 percent of the total energy consumption. This shows that there is no variety in energy consumption structure in Hebei province and energy consumption has many defects. It depends heavily on coal which is non-renewable International Conference on Computational Science and Engineering (ICCSE 2015) © 2015. The authors Published by Atlantis Press 34 energy, so the renewable clean energy strengthened the large market demand, and the development of solar energy utilization technology has a broad prospect. Table.1 Energy consumption and consumption structure in Hebei province Year Total energy consumption Coal Oil Natural gas Electric power Total Proportion Total Proportion Total Proportion Total Proportion 2001 11195.71 10181.38 90.94 914.69 8.17 94.04 0.84 5.60 0.05 2002 12114.29 11125.76 91.84 898.88 7.42 84.80 0.70 4.85 0.04 2003 13404.53 12214.21 91.12 1092.47 8.15 93.83 0.70 4.02 0.03 2004 15297.89 14193.38 92.78 992.83 6.49 100.97 0.66 10.71 0.07 2005 17347.79 15810.78 91.14 1389.56 8.01 130.11 0.75 17.35 0.1

Buildings' energy consumption prediction models based on buildings’ characteristics: Research trends, taxonomy, and performance measures

Building energy consumption (BEC) prediction aims to forecast the future energy consumption distribution, which plays a significant part in achieving energy conservation control, formulating energy conservation strategies, and quantifying the potential of energy conservation. The relevant characteristics of BEC, such as indoor environmental parameters, time information and weather, have a highly significant influence on the predictive accuracy of energy consumption data. Therefore, the relevant characteristics of BEC and historical energy consumption data are typically employed as the database of prediction models. To enhance the rationality of the prediction results and improve the prediction accuracy, a depth BEC prediction model based on the parallel temporal convolutional neural network (PTCN) is proposed in this paper. The PTCN can fully leverage the feature information of two time series, indoor factors and outdoor environmental factors, extract the features respectively for energy consumption prediction, and ultimately combine them through linear fitting. Its parameters are optimised through the least square method to better map the nonlinear relationship between input parameters and energy consumption data. In comparation with other prediction methods, the forecasting accuracy is higher and the performance is improved by more than 20%, which is suitable for BEC prediction.

Accurate prediction of electric energy consumption is crucial for efficient load dispatching, energy utilization, and grid operation. Traditional statistical and classical machine learning methods struggle with the nonlinear nature of energy consumption data, often leading to higher prediction errors. Additionally, deep learning models using a single approach face challenges such as convergence to local minima and poor generalization. This paper proposes a nonlinear ensemble deep learning model for residential energy consumption prediction, incorporating Bayesian optimization for hyperparameter tuning. The model combines Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and 1D Convolutional Neural Networks (1D-CNN), leveraging their powerful nonlinear feature learning capabilities. A k-means clustering approach is used to preprocess and reduce variability in the data, enhancing the ensemble model's performance. The ensemble model was tested on real energy consumption data from two districts in Addis Ababa, showing significant improvements in prediction accuracy with lower MAE, RMSE, and MAPE values compared to single models and un-clustered data. The integration of clustering and Bayesian optimization further enhanced model generalizability and minimized overfitting, demonstrating the effectiveness of a nonlinear approach in capturing complex energy consumption patterns.

Keeping energy costs in budget and operating within available capacities of power distribution and cooling systems is becoming an important requirement for High Performance Computing (HPC) data centers. It is even more important when considering the estimated power requirements for Exascale computing. Power and energy capping are two of emerging techniques aimed towards controlling and efficient budgeting of power and energy consumption within the data center. Implementation of both techniques requires a knowledge of, potentially unknown, power and energy consumption data of the given parallel HPC applications for different numbers of compute servers (nodes).This paper introduces an Adaptive Energy and Power Consumption Prediction (AEPCP) model capable of predicting the power and energy consumption of parallel HPC applications for different number of compute nodes. The suggested model is application specific and describes the behavior of power and energy with respect to the number of utilized compute nodes, taking as an input the available history power/energy data of an application. It provides a generic solution that can be used for each application but it produces an application specific result. The AEPCP model allows for ahead of time power and energy consumption prediction and adapts with each additional execution of the application improving the associated prediction accuracy. The model does not require any application code instrumentation and does not introduce any application performance degradation. Thus it is a high level application energy and power consumption prediction model. The validity and the applicability of the suggested AEPCP model is shown in this paper through the empirical results achieved using two application-benchmarks on the SuperMUC HPC system (the 10th fastest supercomputer in the world, according to Top500 November 2013 rankings) deployed at Leibniz Supercomputing Centre.

Data-driven prediction of energy consumption of district cooling systems (DCS) based on the weather forecast data

To improve the intelligent adjustment ability and energy consumption prediction accuracy of the fresh air system in hospital buildings, this study constructs an energy consumption prediction model based on the Back Propagation Neural Network (BPNN). Meanwhile, it introduces the Genetic Algorithm (GA) and Fuzzy Logic Algorithm (FLA) to optimize the BPNN, thus enhancing the model’s global search ability and robustness. By comparing the proposed optimized model with other models, the study analyzes the advantages of the proposed model in terms of prediction accuracy and convergence speed. Moreover, its practical effectiveness in energy consumption and operational cost optimization is evaluated. The results show that the Genetic Algorithm-Fuzzy Logic Algorithm-Back Propagation (GA-FLA-BP) algorithm performs the best in load prediction, with prediction errors typically below 1.5%, particularly on the 5th and 18th days, demonstrating exceptional performance. Compared to the GA-BP and FLA-BP models, the GA-FLA-BP algorithm exhibits stronger capabilities in handling complex data and uncertainty. Regarding energy consumption and electricity cost optimization, GA-FLA-BP also outperforms other models. Its energy consumption prediction accuracy is 91.5% and an electricity cost prediction accuracy is 90.8%, resulting in savings of 29.2% in energy consumption and 31.2% in costs. Although other algorithms show improvements, GA-FLA-BP remains significantly ahead. Furthermore, the GA-FLA-BP algorithm excels in robustness, consistency, time complexity, and real-time performance. This algorithm demonstrates the highest stability and consistency, the fastest processing speed, and the shortest response time, proving its superior performance in energy consumption management and cost optimization. This study enhances the intelligent adjustment capability of the fresh air system in hospital buildings by optimizing the energy consumption prediction model. Therefore, the study significantly reduces energy consumption and operational costs, improving the efficiency and economy of energy management.

Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining

Short-term energy consumption prediction of buildings is crucial for developing model-based predictive control, fault detection, and diagnosis methods. This study takes a university library in Xi’an as the research object. First, a time-by-time energy consumption prediction model is established under the supervised learning approach, which uses a long short-term memory (LSTM) network and a Multi-Input Multi-Output (MIMO) strategy. The experimental results validate the model’s validity, which is close enough to physical reality for engineering purposes. Second, the potential of the people flows factor in energy consumption prediction models is explored. The results show that people flow has great potential in predicting building energy consumption and can effectively improve the prediction model performance. Third, a diagnostic method, which can recognize abnormal energy consumption data is used to diagnose the unreasonable use of the building during each hour of operation. The method is based on differences between actual and predicted energy consumption data derived from a short-term energy consumption prediction model. Based on actual building operation data, this work is enlightening and can serve as a reference for building energy efficiency management and operation.

In early days the concept of Internet of Things (IOT) was focused on industrial automation only. But as the technology evolves people use IOT in different areas like commercial, health, residential and transportation. It helps to bring all individual devices on a common platform so that controlling and monitoring of individual device from centralized system is possible. Smart home system proves the IOT concept very finely. A home can be called as smart if it is remotely controlled and monitored, automated, secure and where home appliances are smart enough to change their status. This paper presents an Android mobile application implementation of smart home and prediction of electricity energy consumption which uses Wi-Fi and GSM as a communication media to communicate with Beagle Bone Black, the central processing system and for data analysis simple linear regression analysis is used. Smart home system is mainly concerned with the Automation and Security followed further with energy management and prediction of electricity energy consumption.

The prediction of energy consumption is the primary goal of an intelligent energy management system (IEMS). Based on the actual road–traffic conditions, the vehicle energy consumption on the whole planned path can be predicted online by road condition recognition or speed sequence prediction. Because the speed sequence prediction required by the latter cannot accurately reflect the real dynamic characteristics of vehicle speed such as acceleration and deceleration changes due to the random factors of traffic or human beings, which will greatly affect the predicting accuracy, especially on the urban road with complex working conditions. Therefore, based on the analysis of the cumulative relationship between vehicle speed characteristics and energy consumption, this study proposes a prediction method of vehicle driving energy consumption based on the statistical characteristics of vehicle speed, regardless of the accuracy of the prediction of vehicle speed sequence, including the establishment of a long‐term vehicle speed feature prediction model and energy consumption prediction model by BP and SVM algorithms. Finally, its rationality is validated based on the authentic data with an accuracy of about 95%, significantly improved compared with that based on long‐term vehicle speed prediction.

With the continuous development of the global economy and the acceleration of urbanization, the annual energy consumption of buildings also occupies a considerable scale. In order to achieve energy saving and emission reduction in buildings, reasonable energy management for buildings is an important tool to achieve the goal of energy saving and emission reduction. In this paper, an improved Echo State Network method is used to predict building energy consumption. This improved echo state network can not only handle energy consumption data of a single building, but also combine multiple spatially correlated building energy consumption data to further improve the accuracy of energy prediction. The results show that the accuracy of the new model proposed in this paper for building energy consumption prediction is better than that of the classical ESN model and other classical machine learning models, and the model is well suited for end-to-end prediction tasks for multiple buildings. Combined with the clustering algorithm, it can also achieve acceleration for end-to-end prediction tasks.

Office building energy consumption forecast: Adaptive long short term memory networks driven by improved beluga whale optimization algorithm