Energy and AI最新文献

With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning (ML), obtaining power consumption data is becoming more and more important. Collecting real-world energy data using sensors is time consuming, expensive, error-prone and in some situations not possible. Therefore, we present the VA-Creator, a framework to create Virtual Appliances (VAs). These VAs synthesize power consumption patterns (PCPs) based on Neural Networks (NNs) which adapt their architecture to the training data structure to simplify the creation of new VAs. To be able to generate all appliance types available in a typical household we use various kinds of NN, including Multilayer Perceptrons (MLPs), Long Short-Term Memorys (LSTMs) and a specific Generative Adversarial Network (GAN) as well as different ML techniques such as XGBoost, selecting the appropriate technique depending on each appliance’s characteristics. We then compare the results of the ML models against real data and evaluate them by using Dynamic time Warping (DTW) as well as the classification performance of an MLP discriminator as metrics. Additionally, to ensure that the VAs allow to meaningfully train ML models, we use them to generate synthetic data and then train Non intrusive Load Monitoring (NILM) models in an extensive evaluation. The presented evaluation provides evidence that the VA models produce realistic and meaningful results.

Artificial Intelligence (AI) has revolutionized technological development globally, delivering relatively more accurate and reliable solutions to critical challenges across various research domains. This impact is particularly notable within the field of materials science and engineering, where artificial intelligence has catalyzed the discovery of new materials, enhanced design simulations, influenced process controls, and facilitated operational analysis and predictions of material properties and behaviors. Consequently, these advancements have streamlined the synthesis, simulation, and processing procedures, leading to material optimization for diverse applications. A key area of interest within materials science is the development of hydrogen-based electrochemical systems, such as fuel cells and electrolyzers, as clean energy solutions, known for their promising high energy density and zero-emission operations. While artificial intelligence shows great potential in studying both fuel cells and electrolyzers, existing literature often separates them, with a clear gap in comprehensive studies on electrolyzers despite their similarities. This review aims to bridge that gap by providing an integrated overview of artificial intelligence's role in both technologies. This review begins by explaining the fundamental concepts of artificial intelligence and introducing commonly used artificial intelligence-based algorithms in a simplified and clearly comprehensible way, establishing a foundational knowledge base for further discussion. Subsequently, it explores the role of artificial intelligence in materials science, highlighting the critical applications and drawing on examples from recent literature to build on the discussion. The paper then examines how artificial intelligence has propelled significant advancements in studying various types of fuel cells and electrolyzers, specifically emphasizing proton exchange membrane (PEM) based systems. It thoroughly explores the artificial intelligence tools and techniques for characterizing, manufacturing, testing, analyzing, and optimizing these systems. Additionally, the review critically evaluates the current research landscape, pinpointing progress and prevailing challenges. Through this thorough analysis, the review underscores the fundamental role of artificial intelligence in advancing the generation and utilization of clean energy, illustrating its transformative potential in this area of research.

The temperature field within porous media is considerably affected by different boundary conditions, and effective thermal conductivity varies with spatial structure morphologies. At present, traditional prediction methods for the temperature field are expensive and time consuming, particularly for large structures and dimensions, whereas deep learning surrogate models have limitations related to constant boundary conditions and two-dimensional input slices, lacking the three-dimensional topology and spatial correlations. Herein, a constraint-incorporated model using U-Net architecture as the backbone is proposed to predict the temperature field and effective thermal conductivity of sphere-packed porous media, considering diverse external heat fluxes. A total of 510 original samples of temperature fields are generated through lattice Boltzmann method (LBM) simulations, which are further augmented to 33,150 samples using the self-amplification method for the training. Physical prior knowledge is incorporated into the model to constrain the training direction by adding physical constraint terms as well as adaptive weights to the loss function. Input vectors with different heat fluxes and porosities are embedded into latent features for predicting different boundary conditions. Results indicate that the constraint-incorporated model has a mean relative error ranging between 1.1 % and 5.7 % compared with the LBM results in the testing set. It exhibits weak dependence on the database size and substantially reduces computational time, with a maximum speedup ratio of 7.14 × 10⁶. This study presents a deep learning model with physical constraints for predicting heat conduction in porous media, alleviating the burden of extensive experiments and simulations.

Three proposed reaction mechanisms can occur in an amine-CO₂ system: either zwitterionic or termolecular mechanisms for primary/secondary amines and base-catalyzed hydration for tertiary amines. The intricacy of this system hinders the construction of a general model for all types of amines. This study attempts to build an artificial neural network model that predicts the equilibrium solubility of any nonblended aqueous amine-CO₂ system under given operating conditions, regardless of the reaction mechanism. This is a novel approach that has not yet been reported. The amines were characterized using molecular descriptors derived from COSMO theory through density functional theory calculations to incorporate molecular structures as model features. Our model achieved performance metrics (R²) of 0.9645 and 0.9481 for the training and validation sets, respectively. For unfamiliar amines that were absent in both the training and validation sets, our model achieved an R² of 0.8601. Model benchmarking was performed using a previously established thermodynamic model. Interpretations of the model are also provided based on the chosen features. This study also offers exploratory insight into how the molecular structure and operating conditions affect the CO₂ equilibrium solubility in amines. The model developed in this study has the potential to reduce the solvent screening time in determining appropriate amines for larger-scale applications.

A novel approach for generative time series simulation of electricity price scenarios is presented. A “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) generates short-term electricity price scenarios. In particular, the network is capable of generating a 24-dimensional output vector that corresponds to the expected behavior of electricity markets. The model can replace typical approaches from financial mathematics like statistical factor models to model the price distribution around a given forecast. The data cover a 3-year period from 2020 to 2023. Our empirical study is conducted on the EPEX SPOT market in Europe. An electricity price scenario includes the prices of the hourly contracts of a day-ahead auction at the EPEX SPOT power exchange. The model uses multivariate time series as input factors, consisting of point forecasts of electricity prices and fundamental data on generation and load profiles. The architecture of a TSS-CGAN is based on the idea of Conditional Generative Adversarial Networks combined with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory. The model is evaluated using qualitative and quantitative criteria. For the evaluation, 10,000 simulations of a test period are carried out. Qualitative criteria are whether the model follows certain electricity market-specific regularities and depicts them adequately. The quantitative analysis includes common error metric, compared to benchmark models, like DeepAR, Prophet and Temporal Fusion Transformer, the examination of the quantile ranges, the error distribution and a sensitivity analysis. The results show that the TSS-CGAN outperforms benchmark models such as DeepAR by reducing the continuous ranked probability score by 50% and considers market-specific circumstances such as the production of fluctuating energies and reacts correctly to changes in the corresponding variables.