The global artificial intelligence in energy market size is poised to grow by USD 8.06 billion during 2020-2024, decelerating at a CAGR of almost 48% …

The U.S. Department of Energy's (DOE) Office of Fossil Energy (FE) will award up to $1.5 million to winning innovators in a prize challenge to support FE's SMART (Science-informed Machine Learning to Accelerate Real Time Decisions in the Subsurface) initiative. Click here to watch a short video about the SMART Visualization Platform Prize Challenge and learn how to register to take part in this unique software development contest. SMART leverages the expertise of seven national laboratories, as well as industry partners, universities, unconventional field laboratories and carbon storage regional initiatives to realize breakthroughs in understanding the subsurface environment through machine learning. A thorough understanding of the subsurface is necessary to reduce risks and increase the efficiency of enhanced and unconventional oil and natural gas recovery, geothermal energy technologies, geological carbon storage and other operations. Currently, approaches to analyze subsurface data are extremely rigorous, require expert training and are time-consuming and costly.

Latent trajectory inference is a crucial problem within time-series machine learning because the identification immediately provides the interpretability of given data and the relevant systems. Some real-world data such as sequential activity of thousands of neurons [51] have higher dimension than the intrinsic dimension. Other data have lower dimension such as electrophysiological data of voltage measurements in single cells [28]. The latter problem is harder than the former because the observations may be insufficient to describe its dynamics, thus the present paper focuses on this problem to show an advantage of our method. Modeling with latent variables by neural networks have been researched after Recurrent Neural Network [53, 30] was proposed. RNN and its variants (RNNs) such as GRU [9] and LSTM [26] are the benchmark models to learn latent trajectory as the sequence of hidden units to predict or classify the observations.

Back-propagation (BP) is costly to implement in hardware and implausible as a learning rule implemented in the brain. However, BP is surprisingly successful in explaining neuronal activity patterns found along the cortical processing stream. We propose a locally implementable, unsupervised learning algorithm, CLAPP, which minimizes a simple, layer-specific loss function, and thus does not need to back-propagate error signals. The weight updates only depend on state variables of the pre- and post-synaptic neurons and a layer-wide third factor. Networks trained with CLAPP build deep hierarchical representations of images and speech.

The phase-ordering problem of modern compilers has received a lot of attention from the research community over the years, yet remains largely unsolved. Various optimization sequences exposed to the user are manually designed by compiler developers. In designing such a sequence developers have to choose the set of optimization passes, their parameters and ordering within a sequence. Resulting sequences usually fall short of achieving optimal runtime for a given source code and may sometimes even degrade the performance when compared to unoptimized version. In this paper, we employ a deep reinforcement learning approach to the phase-ordering problem. Provided with sub-sequences constituting LLVM's O3 sequence, our agent learns to outperform the O3 sequence on the set of source codes used for training and achieves competitive performance on the validation set, gaining up to 1.32x speedup on previously-unseen programs. Notably, our approach differs from autotuning methods by not depending on one or more test runs of the program for making successful optimization decisions. It has no dependence on any dynamic feature, but only on the statically-attainable intermediate representation of the source code. We believe that the models trained using our approach can be integrated into modern compilers as neural optimization agents, at first to complement, and eventually replace the hand-crafted optimization sequences.

Clustering is frequently used in the energy domain to identify dominant electricity consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting a useful set of clusters however requires extensive experimentation and domain knowledge. While internal clustering validation measures are well established in the electricity domain, limited research is available for external measures. We present a method that distills expert knowledge into competency questions, which we operationalised as external evaluation measures to specify the clustering objective for our application. This approach supported a structured and formal cluster validation process that combined internal and external measures to select a cluster set that is useful for creating residential electricity customer archetypes from electricity meter data in South Africa. We validated the approach in a case study application where we successfully reconstructed customer archetypes previously developed by experts. Our approach enables transparent and repeatable cluster ranking and selection by data scientists, even if they have limited domain knowledge.

Many businesses and industries require accurate forecasts for weekly time series nowadays. The forecasting literature however does not currently provide easy-to-use, automatic, reproducible and accurate approaches dedicated to this task. We propose a forecasting method that can be used as a strong baseline in this domain, leveraging state-of-the-art forecasting techniques, forecast combination, and global modelling. Our approach uses four base forecasting models specifically suitable for forecasting weekly data: a global Recurrent Neural Network model, Theta, Trigonometric Box-Cox ARMA Trend Seasonal (TBATS), and Dynamic Harmonic Regression ARIMA (DHR-ARIMA). Those are then optimally combined using a lasso regression stacking approach. We evaluate the performance of our method against a set of state-of-the-art weekly forecasting models on six datasets. Across four evaluation metrics, we show that our method consistently outperforms the benchmark methods by a considerable margin with statistical significance. In particular, our model can produce the most accurate forecasts, in terms of mean sMAPE, for the M4 weekly dataset.

Boston Dynamics has found a way to use robotic dogs without terrifying people. The maker of "Spot" -- a 71.7-pound, 33.1-inch-tall, four-legged robot -- has teamed up with DroneDeploy, a drone software provider, and Brasfield & Gorrie, one of the nation's largest privately held construction firms, to use the robots to automate construction documentation. "It definitely gets a lot of stares, I don't think out of fear but more out of shock and awe," said Jake Lovelace, a Brasfield & Gorrie innovation specialist. Outfitted with a 360-degree camera, Spot allows workers to autonomously capture data from building interiors and take close-up photos to document the progress a project has made for the bid process or inspection. It's built so that it can navigate rough terrain and also has sensors for noise, carbon dioxide and particulate levels, making it unnecessary for humans to risk going into potentially unsafe places, Lovelace said.

New developments in artificial intelligence (AI) based solutions can help all energy companies put defenders ahead of attackers, while adapting to the …

This paper studies the safe reinforcement learning (RL) problem without assumptions about prior knowledge of the system dynamics and the constraint function. We employ an uncertainty-aware neural network ensemble model to learn the dynamics, and we infer the unknown constraint function through indicator constraint violation signals. We use model predictive control (MPC) as the basic control framework and propose the robust cross-entropy method (RCE) to optimize the control sequence considering the model uncertainty and constraints. We evaluate our methods in the Safety Gym environment. The results show that our approach achieves better constraint satisfaction than baseline safe RL methods while maintaining good task performance. Additionally, we are able to achieve several orders of magnitude better sample efficiency when compared to constrained model-free RL approaches. The code is available at https://github.com/liuzuxin/safe-mbrl.