The rapid adoption of residential solar photovoltaics (PV) has resulted in regular overvoltage events, due to correlated reverse power flows. Currently, PV inverters prevent damage to electronics by curtailing energy production in response to overvoltage. However, this disproportionately affects households at the far end of the feeder, leading to an unfair allocation of the potential value of energy produced. Globally optimizing for fair curtailment requires accurate feeder parameters, which are often unknown. This paper investigates reinforcement learning, which gradually optimizes a fair PV curtailment strategy by interacting with the system. We evaluate six fairness metrics on how well they can be learned compared to an optimal solution oracle. We show that all definitions permit efficient learning, suggesting that reinforcement learning is a promising approach to achieving both safe and fair PV coordination.

In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve prediction accuracy. However, incorporating ML is generally considered to sacrifice interpretability inevitably. In addition, existing hybrid algorithms usually rely on theoretical models with statistical assumptions and focus only on the accuracy of aggregate predictions, and thus suffer from accuracy problems, especially in component estimates. In response to the above issues, this research explores the possibility of improving accuracy without losing interpretability in time series forecasting. We first quantitatively define interpretability for data-driven forecasts and systematically review the existing forecasting algorithms from the perspective of interpretability. Accordingly, we propose the W-R algorithm, a hybrid algorithm that combines decomposition and ML from a novel perspective. Specifically, the W-R algorithm replaces the standard additive combination function with a weighted variant and uses ML to modify the estimates of all components simultaneously. We mathematically analyze the theoretical basis of the algorithm and validate its performance through extensive numerical experiments. In general, the W-R algorithm outperforms all decomposition-based and ML benchmarks. Based on P50_QL, the algorithm relatively improves by 8.76% in accuracy on the practical sales forecasts of JD.com and 77.99% on a public dataset of electricity loads. This research offers an innovative perspective to combine the statistical and ML algorithms, and JD.com has implemented the W-R algorithm to make accurate sales predictions and guide its marketing activities.

Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labor-intensive or costly with success heavily depending on experts' heuristic knowledge. Here, we propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design with high structural diversity and symmetry. Our model increases the generation validity by more than 700\% compared to FTCP, one of the latest structure generators and by more than 45\% compared to our previous CubicGAN model. Density Functional Theory (DFT) calculations are used to validate the generated structures with 1,869 materials out of 2,000 are successfully optimized and deposited into the Carolina Materials Database \url{www.carolinamatdb.org}, of which 39.6\% have negative formation energy and 5.3\% have energy-above-hull less than 0.25 eV/atom, indicating their thermodynamic stability and potential synthesizability.

Optimizing energy consumption for robot navigation in fields requires energy-cost maps. However, obtaining such a map is still challenging, especially for large, uneven terrains. Physics-based energy models work for uniform, flat surfaces but do not generalize well to these terrains. Furthermore, slopes make the energy consumption at every location directional and add to the complexity of data collection and energy prediction. In this paper, we address these challenges in a data-driven manner. We consider a function which takes terrain geometry and robot motion direction as input and outputs expected energy consumption. The function is represented as a ResNet-based neural network whose parameters are learned from field-collected data. The prediction accuracy of our method is within 12% of the ground truth in our test environments that are unseen during training. We compare our method to a baseline method in the literature: a method using a basic physics-based model. We demonstrate that our method significantly outperforms it by more than 10% measured by the prediction error. More importantly, our method generalizes better when applied to test data from new environments with various slope angles and navigation directions.

Driven by the global decarbonization effort, the rapid integration of renewable energy into the conventional electricity grid presents new challenges and opportunities for the battery energy storage system (BESS) participating in the energy market. Energy arbitrage can be a significant source of revenue for the BESS due to the increasing price volatility in the spot market caused by the mismatch between renewable generation and electricity demand. In addition, the Frequency Control Ancillary Services (FCAS) markets established to stabilize the grid can offer higher returns for the BESS due to their capability to respond within milliseconds. Therefore, it is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions. This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit. Then, Proximal Policy Optimization, a model-free deep reinforcement learning algorithm, is employed to learn the optimal bidding strategy from the dynamic environment of the energy market under a continuous bidding scale. The proposed model is trained and validated using real-world historical data of the Australian National Electricity Market. The results demonstrate that our developed joint bidding strategy in both markets is significantly profitable compared to individual markets.

Intelligent robotic systems, such as autonomous vehicles (AVs), are typically architected in a modular fashion and comprised of modules performing detection, tracking, prediction, planning, and control, among others [1, 2, 3, 4, 5, 6, 7, 8]. Modular architectures are generally desirable because of their verifiability, interpretability and generalization performance; however, they also suffer from compounding errors, information bottlenecks, and integration challenges. A promising line of work tackling these issues focuses on making AV stacks more integrated (by relaxing inter-module interfaces) and data-driven (by optimizing modules jointly with respect to their downstream task). For example, in the context of AV perception, recent work has achieved substantial performance gains by jointly training tracking models with detection [9] and prediction models [10, 11]. To extend such a joint, data-driven approach to decision making, existing approaches replace hand-engineered components, e.g., planning and control algorithms, with deep neural networks [12, 13, 14]. As neural networks are differentiable, they can be optimized end-to-end for a final control objective; however, they offer weaker generalization, little to no interpretability or safety guarantees. We introduce DiffStack, a differentiable AV stack with modules for prediction, planning, and control that combines the benefits of modular and data-driven architectures (Figure 1). The prediction module in DiffStack is a learned neural network that predicts the future motion of agents; the planning and control modules are principled, hand-engineered algorithms that produce AV actions given the current world state and motion predictions. Importantly, our hand-engineered planning and control algorithms are differentiable, enabling the training of the upstream prediction module for a downstream control objective by backpropagating gradients through the algorithms.

If you're new to the topic, the hardest part of mastering machine learning is figuring out where to start. It is normal to question the ideal language for machine learning, regardless of whether you are looking to brush up on your machine learning knowledge or completely change careers. Finding the ideal programming language for machine learning is undoubtedly difficult because over 700 distinct programming languages are widely used, and each has advantages and disadvantages. The good news is that you'll start to identify which programming language will best suit a business problem you are trying to address as you start your journey as a machine learning engineer. Which programming language is ideal for machine learning is certainly on your mind if you're considering a career in this area. While numerous options are available for various uses, in this post, we'll focus on the top machine learning languages. It's crucial to comprehend the fundamentals of creating an ML model before discovering why particular programming languages are better suited for ML.

"These things are hard to tip over," geologist Wilson Bonner assures me as the four-wheeled all-terrain vehicle he's piloting tilts suddenly sideways, pitching me toward the churned up mud beneath our wheels. We're grinding up the side of a thickly forested hill in rural Ontario, Canada, on a chilly fall day, heading toward a spot that Bonner's employer, startup KoBold Metals, says represents the marriage of cutting-edge artificial intelligence with one of humanity's oldest industries. We do indeed complete the half-hour trek relatively unmuddied, finally breaking through a ring of broken trees and mangled brush into a swath of bulldozed mud. A black pipe about as wide around as my arm juts out of the ground--the top end of a hole nearly a kilometer deep that was punched into the ground by a truck-sized drilling rig that sits idly nearby. It's not much to look at, but this hole might mark a step into the future of mining, an industry crucial for the world's transition to renewable energy.

It could help tackle climate change, speed up a trip to Mars, and relinquish Vladimir Putin's vice-like energy grip on the West. In short, nuclear fusion would change the world. And that's why scientists across the globe are rejoicing at news coming out of the US that the'holy grail' of unlimited clean power is within touching distance. For the first time ever, researchers are believed to have gained more energy out of a controlled nuclear fusion reaction than they put in. Excitement: Scientists across the globe are rejoicing at news coming out of the US that the'holy grail' of unlimited clean power is within touching distance.

Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate'') can be produced by employing a feedback loop, whereby the model is trained to predict forward one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth. In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. Our case study uses reservoir computing, a machine-learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while reservoir computers trained without regularization are unstable. Compared with other regularization techniques that yield stability in some cases, we find that both short-term and climate predictions from reservoir computers trained with noise or with LMNT are substantially more accurate. Finally, we show that the deterministic aspect of our LMNT regularization facilitates fast hyperparameter tuning when compared to training with noise.