A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions

Short-term building energy predictions serve as one of the fundamental tasks in building operation management.While large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions,few studies have addressed the potential data shortage problem in developing data-driven models.One promising solution is data augmentation,which aims to enrich existing building data resources for reliable predictive modeling.This study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy predictions.Two types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers respectively.Data experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 buildings.The results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples,which in turns helps to enhance the accuracy in short-term building energy predictions.The average performance enhancement ratios in terms of CV-RMSE range between 12%and 18%.Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy data.The research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management.

NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.

Enhancing Urban Intelligence Energy Management: Innovative Load Forecasting Techniques for Electrical Networks

Energy sustains the world, yet fossil fuels, a finite resource, are dwindling. This necessitates a shift towards more sustainable energy sources, such as electricity. Accurate load forecasting is crucial in today’s global energy landscape, as it helps predict various aspects such as production, revenue, consumption, economic conditions, weather impacts, power system utilization, customer demand, and economic growth. For instance, an increase in electricity demand within a country often signifies a boost in industry and production, leading to economic progress and reduced unemployment. This project aims to enhance prediction accuracy through meticulous input filtering, taking into account factors like population growth, planned loads, inflation, and competitive pricing pressures from producers. Despite inherent prediction errors, efforts are made to minimize these discrepancies. This paper introduces a novel combined method for mid-term energy forecasting. To demonstrate its efficacy, real data from the past ten months, collected from subscribers of the Kerman distribution company, was used to forecast energy consumption over the next ten days. The innovative method, which integrates multiple forecasting techniques and robust filters, significantly improves forecasting precision. The following error metrics were recorded for the proposed method: MSE: 0.009, MAE: 0.083, MAPE: 0.776, RMSE: 0.095, AE: 0.013.

Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.

A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.

Application of four machine-learning methods to predict short-horizon wind energy

Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.

Energy Balance-related Behaviors Are Related to Cardiometabolic Parameters and Predict Adiposity in 8-14-year-old Overweight Chinese Children One Year Later

To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.

A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are minimized.However,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)levels.Therefore,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous VMs.This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total cost.Specifically,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective manner.Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns.

Energy Prediction in IoT Systems Using Machine Learning Models

The Internet of Things (IoT) technology has been developed fordirecting and maintaining the atmosphere in smart buildings in real time. Inorder to optimise the power generation sector and schedule routine maintenance,it is crucial to predict future energy demand. Electricity demandforecasting is difficult because of the complexity of the available demandpatterns. Establishing a perfect prediction of energy consumption at thebuilding’s level is vital and significant to efficiently managing the consumedenergy by utilising a strong predictive model. Low forecast accuracy is justone of the reasons why energy consumption and prediction models havefailed to advance. Therefore, the purpose of this study is to create an IoTbasedenergy prediction (IoT-EP) model that can reliably estimate the energyconsumption of smart buildings. A real-world test case on power predictionsis conducted on a local electricity grid to test the practicality of the approach.The proposed (IoT-EP) model selects the significant features as input neurons,the predictable data is selected as output nodes, and a multi-layer perceptronis constructed along with the features of the Convolution Neural Network(CNN) algorithm. The analysis of the proposed IoT-EP model has higheraccuracy of 90%, correlation of 89%, and variance of 16% in less training timeof 29.2 s, and with a higher prediction speed of 396 (observation/sec). Whencompared to existing models, the results showed that the proposed (IoT-EP)model outperforms with a satisfactory level of accuracy in predicting energyconsumption in smart buildings.

Long-Term Energy Forecasting System Based on LSTM and Deep Extreme Machine Learning

Due to the development of diversified and flexible building energy resources,the balancing energy supply and demand especially in smart build-ings caused an increasing problem.Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption.Recently,their management has a great significance as resources become scarcer and their emissions increase.In this article,we propose an intelligent energy forecasting method based on hybrid deep learning,in which the data collected by the smart home through meters is put into the pre-evaluation step.Next,the refined data is the input of a Long Short-Term Memory(LSTM)network,which captures the spatio-temporal correlations from the sequence and generates the feature maps.The output feature map is passed into a Deep Extreme Machine Learning network(with seven hidden layers)for learning,which provides the final prediction.Compared to existing techniques,the LSTM-DELM model offers better prediction results.The simulation values demonstrate the superior performance of the proposed model.

Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and reliable.The RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy demands.Since energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power resources.Predicting energy consumption of RE and NRE sources using effective models becomes necessary.With this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power sources.Actual data may influence the prediction performance of the results in prediction approaches.The proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter optimization.In addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL models.The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic benefits.This study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model accuracy.Various models were developed and trained with different preprocessing stages.Finally,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction errors.Results indicated that models trained on raw data generally performed better than those on stripped data.The Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling techniques.The study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.

Improved artificial neural network method for predicting photovoltaic output performance

To ensure the safety and stability of power grids with photovoltaic(PV)gen eration integrati on,it is necessary to predict the output perform a nee of PV modules un der varyi ng operating con ditions.In this paper,an improved artificial neural network(ANN)method is proposed to predict the electrical characteristics of a PV module by combining several neural networks under different environmental conditions.To study the dependenee of the output performance on the solar irradianee and temperature,the proposed neural network model is composed of four neural networks,it called multineural network(MANN).Each neural network consists of three layers,in which the input is solar radiation,and the module temperature and output are five physical parameters of the single diode model.The experimental data were divided into four groups and used for training the neural networks.The electrical properties of PV modules,including l-V curves,PV curves,and normalized root mean square error,were obtained and discussed.The effectiveness and accuracy of this method is verified by the experimental data for d iff ere nt types of PV modules.Compared with the traditional single-ANN(SANN)method,the proposed method shows be社er accuracy under different operating conditions.

An improved transfer learning strategy for short-term cross-building energy prediction usingdata incremental

The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%.

Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.

Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

An Overview to the Concept of Smart Coupling and Battery Management for Grid Connected Photovoltaic Battery System

The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems （INES）, Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system （PEMS）.

A Prediction of the Excess Partial Molar Free Energies of MgCl_2 in the KCI-MgCl_2-LiCl Molten Salt System Containing MgCl_2 below 0.5 from Thermodynamic Properties of Binary Systems

The thermodynamical properties of MgCl_2 in KCI-MgCl_2-LiCl molten electrolytes containing MgCl_2 below 0.5 (mole fraction, the same below) have been determined from the interchange energies of two binary systems KCI-MgCl_2 and LiCI-MgCl_2, by means of a model on the assumptions that the electrolytes in the solution are treated as independent particles instead of their ion forms and the interchange energy between the component pair KCI-LiCl is ignored when compared with those of component pairs KCl-MgCl_2 and MgCl_2-LiCl. The interchange energies, wKCl-MgCl_2 and wMgcCl_2-Licl, are obtained as-70000 and -13800 J.mol-1, from the corresponding binary solutions, respectively.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. These represent substantial opportunities considering that they only require software adjustments to implement.