Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting monitor. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change

Better wind forecasting using Evolutionary Neural Architecture search driven Green Deep Learning

Climate Change heavily impacts global cities, the downsides of which can be minimized by adopting renewables like wind energy. However, despite its advantages, the nonlinear nature of wind renders the forecasting approaches to design and control wind farms ineffective. To expand the research horizon, the current study a) analyses and performs statistical decomposition of real-world wind time-series data, b) presents the application of Long Short-Term Memory (LSTM) networks, Nonlinear Auto-Regressive (NAR) models, and Wavelet Neural Networks (WNN) as efficient models for accurate wind forecasting with a comprehensive comparison among them to justify their application and c) proposes an evolutionary multi-objective strategy for Neural Architecture Search (NAS) to minimize the computational cost associated with training and inferring the networks which form the central theme of Green Deep Learning. Balancing the trade-off between parsimony and prediction accuracy, the proposed NAS strategy could optimally design NAR, WNN, and LSTM models with a mean test accuracy of 99%. The robust methodologies discussed in this work not only accurately model the wind behavior but also provide a green & generic approach for designing Deep Neural Networks

Modelling hospital admission rates in São Paulo, Brazil : Lee-Carter model vs. neural networks

Mestrado Bolonha em Actuarial ScienceIn Brazil, hospital admissions represent almost 50% of the total claims cost of health insurance companies while they only represent 1% of the total medical procedures. Therefore, modeling hospital admissions is extremely useful for health insurers to assess their claim costs over time and actuaries should be capable to include that information in their analyses, in order to preserve the financial sustainability of the companies. This dissertation analyses the use of the Lee-Carter model for predicting the general level of hospital admissions in the state of São Paulo, Brazil, using the traditional ARIMA model and contrasting it with the LSTM neural network. Publicly available data between the years 2008 and 2019, divided by gender, were used. The function auto.arima from the R package forecast was used to find the best ARIMA model for the data while the LSTM neural network model was searched in a combination of 20 models, varying the learning rate and decay factor. The results showed that the LSTM model and the ARIMA have similar RMSE and MAE performance.No Brasil, hospitalizações representam quase 50% dos custos totais de sinistros em operadoras de planos de saúde enquanto representam apenas 1% dos procedimentos médicos. Portanto, estimar hospitalizações é extremamente útil para que operadoras de planos de saúde possam estimar seus custos ao longo do tempo e atuários devem ser capazes de incluir essas informações em suas análises para garantir a sustentabilidade financeira das companhias. Essa dissertação analisa o uso do modelo de Lee-Carter para prever o nível geral de hospitalizações no estado de São Paulo, Brasil, utilizando o modelo ARIMA tradicional e comparando-o com a rede neuronal LSTM. Dados públicos entre os anos de 2008 e 2019, divididos por sexo, foram utilizados. A função auto.arima do pacote R forecast foi utilizada para encontrar o melhor modelo ARIMA enquanto que a rede neuronal LSTM foi selecionada entre a combinação de 20 modelos, variando a learning rate e o decay factor. Os resultados mostraram que o modelo LSTM e o modelo ARIMA possuem RMSE e MAE similares.info:eu-repo/semantics/publishedVersio

A Systematic Survey on Deep Generative Models for Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for the graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted

Enhancing statistical wind speed forecasting models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū Campus, New Zealand

In recent years, wind speed forecasting models have seen significant development and growth. In particular, hybrid models have been emerging since the last decade. Hybrid models combine two or more techniques from several categories, with each model utilizing its distinct strengths. Mainly, data-driven models that include statistical and Artificial Intelligence/Machine Learning (AI/ML) models are deployed in hybrid models for shorter forecasting time horizons (< 6hrs). Literature studies show that machine learning models have gained enormous potential owing to their accuracy and robustness. On the other hand, only a handful of studies are available on the performance enhancement of statistical models, despite the fact that hybrid models are incomplete without statistical models. To address the knowledge gap, this thesis identified the shortcomings of traditional statistical models while enhancing prediction accuracy. Three statistical models are considered for analyses: Grey Model [GM(1,1)], Markov Chain, and Holt’s Double Exponential Smoothing models. Initially, the problems that limit the forecasting models' applicability are highlighted. Such issues include negative wind speed predictions, failure of predetermined accuracy levels, non-optimal estimates, and additional computational cost with limited performance. To address these concerns, improved forecasting models are proposed considering wind speed data of Palmerston North, New Zealand. Several methodologies have been developed to improve the model performance and fulfill the necessary and sufficient conditions. These approaches include adjusting dynamic moving window, self-adaptive state categorization algorithm, a similar approach to the leave-one-out method, and mixed initialization method. Keeping in view the application of the hybrid methods, novel MODWT-ARIMA-Markov and AGO-HDES models are further proposed as secondary objectives. Also, a comprehensive analysis is presented by comparing sixteen models from three categories, each for four case studies, three rolling windows, and three forecasting horizons. Overall, the improved models showed higher accuracy than their counter traditional models. Finally, the future directions are highlighted that need subsequent research to improve forecasting performance further

Ensemble model-based method for time series sensors’ data validation and imputation applied to a real waste water treatment plant

Intelligent Decision Support Systems (IDSSs) integrate different Artificial Intelligence (AI) techniques with the aim of taking or supporting human-like decisions. To this end, these techniques are based on the available data from the target process. This implies that invalid or missing data could trigger incorrect decisions and therefore, undesirable situations in the supervised process. This is even more important in environmental systems, which incorrect malfunction could jeopardise related ecosystems. In data-driven applications such as IDSS, data quality is a basal problem that should be addressed for the sake of the overall systems’ performance. In this paper, a data validation and imputation methodology for time-series is presented. This methodology is integrated in an IDSS software tool which generates suitable control set-points to control the process. The data validation and imputation approach presented here is focused on the imputation step, and it is based on an ensemble of different prediction models obtained for the sensors involved in the process. A Case-Based Reasoning (CBR) approach is used for data imputation, i.e., similar past situations to the current one can propose new values for the missing ones. The CBR model is complemented with other prediction models such as Auto Regressive (AR) models or Artificial Neural Network (ANN) models. Then, the different obtained predictions are ensembled to obtain a better prediction performance than the obtained by each individual prediction model separately. Furthermore, the use of a meta-prediction model, trained using the predictions of all individual models as inputs, is proposed and compared with other ensemble methods to validate its performance. Finally, this approach is illustrated in a real Waste Water Treatment Plant (WWTP) case study using one of the most relevant measures for the correct operation of the WWTPs IDSS, i.e., the ammonia sensor, and considering real faults, showing promising results with improved performance when using the ensemble approach presented here compared against the prediction obtained by each individual model separately.The authors acknowledge the partial support of this work by the Industrial Doctorate Programme (2017DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (published version