Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Dynamic pricing models for electronic business

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing

Automation, Decision Making and Business to Business Pricing

In a world going towards automation, I ask whether salespeople making pricing decisions in a high human interaction environment such as business to business (B2B) retail, could be automated, and under what conditions it would be most beneficial. I propose a hybrid approach to automation that combines the expert salesperson and an artificial intelligence model of the salesperson in making pricing decisions in B2B. The hybrid approach preserves individual and organizational knowledge both by learning the expert's decision making behavior and by keeping the expert in the decision making process for decisions that require human judgment. Using sales transactions data from a B2B aluminum retailer, I create an automated version of each salesperson, that learns the salesperson's pricing policy based on her past pricing decisions. In a field experiment, I provide salespeople in the B2B retailer with their own model's price recommendations through their CRM system in real-time, and allow them to adjust their original pricing accordingly. I find that despite the loss of non-codeable information that is available to the salesperson but not to the model, providing the model's price increases profits for treated quotes by as much as 10% relative to a control condition, which translates to approximately $1.3 million in yearly profits. Using a counterfactual analysis, I also find that a hybrid pricing approach, that follows the model's pricing most of time, but defers to the salesperson's pricing when the model is missing important information is more profitable than pure automation or pure reliance on the salesperson's pricing. I find that in most cases the model's scalability and consistency lead to better pricing decisions that translate to higher profits, but when pricing uncommon products or pricing for unfamiliar clients it is best to use human judgment. I investigate different ways, including machine learning methods, to model the salesperson's behavior and to combine salespeople's expertise as reflected by their automated representations, and discuss implications for automation of tasks that involve soft skills