solar energy

The UK is now using AI to predict solar power and lower energy bills

New Scientist

The UK's forecasts for solar power generation have become far more accurate through the use of artificial intelligence, in a development that could lower energy bills and carbon emissions. The country's energy system is becoming more reliant sources of electricity with a variable output. Renewables like wind and solar, which depend on the weather, provided 36 per cent of our electricity at the start of this year, up from 7 per cent in 2009. "The growth in solar was much, much more fast-paced than anyone anticipated," says …

Machine Learning vs. Climate Change: AI for the Greener Good


Climate change is one of the most pressing issues of our time. Despite increasing global consensus about the urgency of reducing emissions since the 1980s, they continue to rise relentlessly. We look to technology to deliver us from climate change, preferably without sacrificing economic growth. Our optimistic--some would say techno-utopian--visions of the future involve vast arrays of solar panels, machines that suck carbon dioxide back out of the atmosphere, and replacing fossil fuels for transport and heating with electricity generated by renewable means. This is nothing less than rebuilding our civilization on stable, sustainable foundations.

The RoboBee flies solo


In the Harvard Microrobotics Lab, on a late afternoon in August, decades of research culminated in a moment of stress as the tiny, groundbreaking Robobee made its first solo flight. Graduate student Elizabeth Farrell Helbling, Ph.D.'19, and postdoctoral fellow Noah T. Jafferis, Ph.D. from Harvard's Wyss Institute for Biologically Inspired Engineering, the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Graduate School of Arts and Sciences caught the moment on camera. Helbling, who has worked on the project for six years, counted down: "Three, two, one, go." The bright halogens switched on and the solar-powered Robobee launched into the air. For a terrifying second, the tiny robot, still without on-board steering and control, careened towards the lights.

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting Machine Learning

Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dot-product self attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canonical Transformer grows quadratically with sequence length $L$, making modeling long time series infeasible. In order to solve these two issues, we first propose convolutional self attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism. Then, we propose LogSparse Transformer with only $O(L(\log L)^{2})$ memory cost, improving the time series forecasting in finer granularity under constrained memory budget. Our experiments on both synthetic data and real-world datasets show that it compares favorably to the state-of-the-art.

Incredible video reveals the tiny solar-powered 'RoboBEE'

Daily Mail - Science & tech

To achieve untethered flight, this latest iteration of the Robobee underwent several important changes, including the addition of a second pair of wings. The change from two to four wings, along with less visible changes to the actuator and transmission ratio, made the vehicle more efficient, gave it more lift, and allowed us to put everything we need on-board without using more power, the team said. The extra lift, with no additional power requirements, allowed the researchers to cut the power cord -- which has kept the Robobee tethered for nearly a decade -- and attach solar cells and an electronics panel to the vehicle. The solar cells, the smallest commercially available, weigh 10 milligrams each and get 0.76 milliwatts per milligram of power when the sun is at full intensity. The Robobee X-Wing needs the power of about three Earth suns to fly, making outdoor flight out of reach for now.

Tiny flying insect robot has four wings and weighs under a gram

New Scientist

A solar-powered flying robot has become the lightest machine capable of flying without an attached power source. Weighing just 259 milligrams, the insect-inspired RoboBee X-Wing has four wings that flap at a rate of 170 times per second. It has a wingspan of 3.5 centimetres and is 6.5 cemtimetres high. The flying robot was developed by Noah Jafferis and colleagues at Harvard University. Its wings are controlled by two muscle-like plates that contract when voltage passes through them.

An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning Machine Learning

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.

Machine Learning on EPEX Order Books: Insights and Forecasts Machine Learning

Forecasting electricity prices is an important task in an energy utility and needed not only for proprietary trading but also for the optimisation of power plant production schedules and other technical issues. A promising approach in power price forecasting is based on a recalculation of the order book using forecasts on market fundamentals like demand or renewable infeed. However, this approach requires extensive statistical analysis of market data. In this paper, we examine if and how this statistical work can be reduced using machine learning. Our paper focuses on two research questions: - How can order books from electricity markets be included in machine learning algorithms? - How can order-book-based spot price forecasts be improved using machine learning? We consider the German/Austrian EPEX spot market for electricity. There is a daily auction for electricity with delivery the next day. All 24 hours of the day are traded as separate products.

Harnessing Potential of Artificial Intelligence In Energy and Oil & Gas


The energy industry is undergoing a rapid transformation in recent past owing to the enhanced role of renewables and enhanced data-driven models making the value chain smarter. In the context of the primary constituents of this sector comprising of coal, power, renewables, solar energy, oil, and gas, there is a huge role AI can play. The biggest disruption in power in recent times is in the smart grid which is quite flexible in comparison to the traditional grid. AI can be a huge enabler in the form of providing optimal configurations etc to create a really smart and efficient grid. By thorough analysis of data related to losses AI can help prevent transmission and distribution losses.

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends Machine Learning

Deep learning (DL) has solved a problem that as little as five years ago was thought by many to be intractable - the automatic recognition of patterns in data; and it can do so with accuracy that often surpasses human beings. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners trying to make sense out of the flood of data that now inundates our society. As public awareness of the efficacy of DL increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge produced by experts in the field. Where does one start? How does one determine if a particular model is applicable to their problem? How does one train and deploy such a network? A primer on the subject can be a good place to start. With that in mind, we present an overview of some of the key multilayer ANNs that comprise DL. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is becoming critical to many computer applications, we include a section on using neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where DL has emerged as a game-changing technology: anomalous behavior detection in financial applications or in financial time-series forecasting, predictive and prescriptive analytics, medical image processing and analysis and power systems research. The thrust of this review is to outline emerging areas of application-oriented research within the DL community as well as to provide a reference to researchers seeking to use it in their work for what it does best: statistical pattern recognition with unparalleled learning capacity with the ability to scale with information.