The spread of the novel coronavirus that causes COVID-19 has prompted state and local governments around the U.S. to institute shelter-in-place orders and business closures. As millions suddenly find themselves confined to their homes, the shift has strained not only internet service providers, streaming platforms, and online retailers, but the utilities supplying power to the nation's electrical grid, as well. U.S. electricity use on March 27, 2020 was 3% lower than it was on March 27, 2019, a loss of about three years of sales growth. Peter Fox-Penner, director of the Boston University Institute for Sustainable Energy, asserted in a recent op-ed that utility revenues will suffer because providers are halting shutoffs and deferring rate increases. Moreover, according to research firm Wood Mackenzie, the rise in household electricity demand won't offset reduced business electricity demand, mainly because residential demand makes up just 40% of the total demand across North America.

This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion. Deep Reinforcement Learning will be considered in greater detail in part 3 of this series. For the purpose of this article I will consider AI to cover Machine Learning and Deep Learning. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task.

The AI engine supports a self-operating building that requires no human intervention. What inspired you to launch BrainBox AI? My journey into HVAC technology began while working on energy efficiency projects throughout North America and Europe. During this stage of my life, I dealt with the technology in a plethora of buildings. These were buildings of different sizes and purpose, anything from hotels all the way to data centers. It quickly became apparent to me that continuous commissioning approaches would generate consistent energy savings but would require extensive amounts of both financial and human capital.

Continuing efforts to have a robust corporate governance system and ensure high level of compliance, the ministry is also in the process of having an advanced MCA 21 portal. The portal is used for submission of requisite filings under the companies law and is also a repository of data on corporates in the country. Corporate Affairs Secretary Injeti Srinivas told PTI that once the third version of MCA 21 becomes fully operational, the portal would make it "almost impossible for a shell company to survive." Generally, shell companies are those which are not complying with regulations and many such entities are allegedly used for money laundering and other illegal activities. Noting that the third version of the portal might be fully operational in a year from now, the secretary said the ecosystem would have zero tolerance for non-compliance.

Increasing the penetration of variable generation has a substantial effect on the operational reliability of power systems. The higher level of uncertainty that stems from this variability makes it more difficult to determine whether a given operating condition will be secure or insecure. Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure. This paper proposes using a sparse weighted oblique decision tree to learn accurate, understandable, and embeddable security rules that are linear and can be extracted as sparse matrices using a recursive algorithm. These matrices can then be easily embedded as security constraints in power system economic dispatch calculations using the Big-M method. Tests on several large datasets with high renewable energy penetration demonstrate the effectiveness of the proposed method. In particular, the sparse weighted oblique decision tree outperforms the state-of-art weighted oblique decision tree while keeping the security rules simple. When embedded in the economic dispatch, these rules significantly increase the percentage of secure states and reduce the average solution time.

Machine learning is leading to numerous changes in the energy industry. The Department of Energy recently announced that it is taking steps to accelerate the integration of machine learning technology in energy research and development. The head of the Department of Energy announced that they will be investing $30 million in artificial intelligence and machine learning algorithms. The new programs will have multiple purposes. One of the biggest goals is to use machine learning to facilitate the development of new renewable energy technologies.

In this paper we present a sequential hypothesis test for the detection of general jump size distrubution. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analyzed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the crude oil data set, and the deterministic component is implemented to improve the Barndorff-Nielsen and Shephard model, a commonly used stochastic model for derivative and commodity market analysis.

When it comes to your next-door neighbors, maybe it's better that way. As we operationalize machine learning (ML) and AI systems, end-users need to know how decisions are made and why actions are taken. What I hear often from clients looking at adopting AI or users in the field that work with AI-based decision making is that they don't trust the black box paradigm of AI. If AI is "learning" and "evolving" based on acquired data, and they can't see its logic flow, they're not comfortable with it and do not want to rely on its decisions or recommendations. I recently discussed this very issue with a client that had developed an AI to assist human teams determine bid ranges based on strategic fit, expected economic return, and competitive intelligence when bidding for oil and gas exploration leases.

Commercial buildings are voracious consumers of energy. The buildings and building construction sectors contribute around 30% of global energy consumption and almost 40% of CO2 emissions (direct and indirect), according to the IEA. Sustainable buildings are more than trendy, they're key to reducing humans' carbon footprint on the planet. Building owners deploy smart building solutions and building-management systems to not only cut operations costs and comply with regulations but also to do their part in reducing carbon emissions without sacrificing occupant comfort in the process. So how do you make an already smart, efficient building smarter and more efficient?

Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy Method (CEM), for planning a sequence of actions. To decide on an action to take, CEM conducts a search for the action sequence with the highest return according to the dynamics model and reward. Action sequences are typically randomly sampled from an unconditional Gaussian distribution and evaluated on the environment. This distribution is iteratively updated towards action sequences with higher returns. However, this planning method can be very inefficient, especially for high-dimensional action spaces. An alternative line of approaches optimize action sequences directly via gradient descent, but are prone to local optima. We propose a method to solve this planning problem by interleaving CEM and gradient descent steps in optimizing the action sequence. Our experiments show faster convergence of the proposed hybrid approach, even for high-dimensional action spaces, avoidance of local minima, and better or equal performance to CEM. Code accompanying the paper is available here 1 .