A lot of nat gas analysts would at times reference EIA's Nuclear Capacity Outage (NCO henceforth), yet I haven't seen anyone do a detailed explanation of how they apply it toward an objective bias in implied Nat Gas demand, i.e. Fair Value bias going forward expected by traders paying attention to NCO. So I got curious, and first look at NG prices vs. YOY change in NCOs: So it looks like there is likely somewhat of a rough relationship, that some traders are paying attention to it. Then the next step would be an attempt toward precision via Time Series Analysis. So, what I'd do here is a 2 Step Machine Learning process of 1) Forecast expected NCO for the rest of 2019, then apply that to estimate Natural Gas futures fair value bias going forward.

The population model is a standard way to represent large-scale decentralized distributed systems, in which agents with limited computational power interact in randomly chosen pairs, in order to collectively solve global computational tasks. In contrast with synchronous gossip models, nodes are anonymous, lack a common notion of time, and have no control over their scheduling. In this paper, we examine whether large-scale distributed optimization can be performed in this extremely restrictive setting. We introduce and analyze a natural decentralized variant of stochastic gradient descent (SGD), called PopSGD, in which every node maintains a local parameter, and is able to compute stochastic gradients with respect to this parameter. Every pair-wise node interaction performs a stochastic gradient step at each agent, followed by averaging of the two models. We prove that, under standard assumptions, SGD can converge even in this extremely loose, decentralized setting, for both convex and non-convex objectives. Moreover, surprisingly, in the former case, the algorithm can achieve linear speedup in the number of nodes $n$. Our analysis leverages a new technical connection between decentralized SGD and randomized load-balancing, which enables us to tightly bound the concentration of node parameters. We validate our analysis through experiments, showing that PopSGD can achieve convergence and speedup for large-scale distributed learning tasks in a supercomputing environment.

Stuart Russell is a leading AI researcher who literally wrote (well, co-authored) the top textbook on the topic. He has also, for the last several years, been warning that his field has the potential to go catastrophically wrong. In a new book, Human Compatible, he explains how. AI systems, he notes, are evaluated by how good they are at achieving their objective: winning video games, writing humanlike text, solving puzzles. If they hit on a strategy that fits that objective, they will run with it, without explicit human instruction to do so.

We're about to enter the "exascale era" of computing, which could have widespread positive impacts for governments, businesses and society at large. The U.S. Department of Energy recently announced contracts for supercomputers that will each provide more than an exaflop of performance. That means they can perform 1 quintillion mathematical calculations (called floating-point operations, or flops) every second. The work done on these systems will impact all our lives. And the technologies developed for them will enhance computing systems at all scales, transforming the future of the enterprise.

Municipalities, just like the industrial and commercial sectors, are coming under increased pressure to reduce their energy consumption and outputs, not to mention the need to reduce costs overall. Municipal buildings and services have a huge energy savings potential, which can reduce their overall energy consumption and energy costs. At Maximpact our expert teams have assisted municipalities all over the world to identify their energy saving capacity in various sectors. As cities around the world become more urbanised and populations grow, the pressure of cities to find sustainable solutions to serve their communities is only going to increase. Changes to municipalities in becoming more energy efficient and using artificial intelligence to manage energy resources are part of a global trend of developing smart cities. Smart cities are looking to the future to redefine their energy outputs in cleaner, more sustainable and more cost-efficient ways.

Sustainability became the most important component of world development, as countries worldwide fight the battle against the climate change. To understand the effects of climate change, the ecological footprint, along with the biocapacity should be observed. The big part of the ecological footprint, the carbon footprint, is most directly associated with the energy, and specifically fuel sources. This paper develops a time series vector autoregression prediction model of the ecological footprint based on energy parameters. The objective of the paper is to forecast the EF based solely on energy parameters and determine the relationship between the energy and the EF. The dataset included global yearly observations of the variables for the period 1971-2014. Predictions were generated for every variable that was used in the model for the period 2015-2024. The results indicate that the ecological footprint of consumption will continue increasing, as well as the primary energy consumption from different sources. However, the energy consumption from coal sources is predicted to have a declining trend.

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the `optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits.

Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While most work has focused on supervised algorithms, unsupervised approaches can be more interesting and of practical use in real case scenarios. Specifically, they do not require labelled training data to be acquired from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Bayesian hierarchical mixture models. In particular, we develop a new method based on Gaussian Latent Dirichlet Allocation (GLDA) in order to extract global components that summarise the energy signal. These components provide a representation of the consumption patterns. Designed to cope with big data, our algorithm, unlike existing NILM ones, does not focus on appliance recognition. To handle this massive data, GLDA works online. Another novelty of this work compared to the existing NILM is that the data involves different utilities (e.g, electricity, water and gas) as well as some sensors measurements. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns.

Abstract--Single-hidden layer feed forward neural networks (SLFNs) are widely used in pattern classification problems, but a huge bottleneck encountered is the slow speed and poor perf or-mance of the traditional iterative gradient-based learnin g algorithms. Although the famous extreme learning machine (ELM) has successfully addressed the problems of slow convergenc e, it still has computational robustness problems brought by inp ut weights and biases randomly assigned. Thus, in order to over - come the aforementioned problems, in this paper, a novel typ e neural network based on Gegenbauer orthogonal polynomials, termed as GNN, is constructed and investigated. This model c ould overcome the computational robustness problems of ELM, whi le still has comparable structural simplicity and approximat ion capability. Based on this, we propose a regularized weights direct determination (R-WDD) based on equality-constrain ed optimization to determine the optimal output weights. The R - WDD tends to minimize the empirical risks and structural ris ks of the network, thus to lower the risk of over fitting and impro ve the generalization ability. This leads us to a the final GNN wi th R-WDD, which is a unified learning mechanism for binary and multi-class classification problems. Finally, as is verifie d in the various comparison experiments, GNN with R-WDD tends to have comparable (or even better) generalization performan ces, computational scalability and efficiency, and classificati on robustness, compared to least square support vector machine ( LS-SVM), ELM with Gaussian kernel. ESEARCHES on artificial feed-forward neural networks (FNNs) have become increasingly active and popular, for it is one of the most powerful tools in artificial intelligenc e field.

State-of-the-art unsupervised multilingual models (e.g., multilingual BERT) have been shown to generalize in a zero-shot cross-lingual setting. This generalization ability has been attributed to the use of a shared subword vocabulary and joint training across multiple languages giving rise to deep multilingual abstractions. We evaluate this hypothesis by designing an alternative approach that transfers a monolingual model to new languages at the lexical level. More concretely, we first train a transformer-based masked language model on one language, and transfer it to a new language by learning a new embedding matrix with the same masked language modeling objective--freezing parameters of all other layers. This approach does not rely on a shared vocabulary or joint training. However, we show that it is competitive with multilingual BERT on standard cross-lingual classification benchmarks and on a new Cross-lingual Question Answering Dataset (XQuAD). Our results contradict common beliefs of the basis of the generalization ability of multilingual models and suggest that deep monolingual models learn some abstractions that generalize across languages. We also release XQuAD as a more comprehensive cross-lingual benchmark, which comprises 240 paragraphs and 1190 question-answer pairs from SQuAD v1.1 translated into ten languages by professional translators.