Non-Intrusive Load Monitoring (NILM) uses one smart meter at the power feed to disaggregate the states of a set of appliances. Multiple NILM meters are deployed to achieve high monitoring accuracy in large-scale power systems. Our work studies the tradeoff between monitoring accuracy and meter deployment, in a quantitative and extensible way. In particular, we introduce a clearness function as an abstract indicator of expected monitoring accuracy given any NILM method, and then showcase two concrete constructions. With the notation of a clearness function, we propose solutions to the smart meter deployment problem (SMDP), that is, the problem of finding a deployment scheme with minimum number of meters while attaining a required monitoring accuracy. Theoretically, SMDP is shown NP-hard and a polynomial-time approximation scheme (PTAS) is proposed in this paper. For evaluation, we show that our proposed scheme is efficient and effective in terms of approximation ratio and running time. On real and simulated datasets, our proposed framework achieves a higher monitoring accuracy at a much lower cost, outperforming common baseline algorithms.

As cloud computing gains in popularity, understanding the patterns and structure of its loads is increasingly important in order to drive effective resource allocation, scheduling and pricing decisions. These efficiency increases are then associated with a reduction in the data center environmental footprint. Existing models have only treated a single resource type, such as CPU, or memory, at a time. We offer a sophisticated machine learning approach to capture the joint-distribution. We capture the relationship among multiple resources by carefully fitting both the marginal distributions of each resource type as well as the non-linear structure of their correlation via a copula distribution. We investigate several choices for both models by studying a public data set of Google data-center usage. We show the Burr XII distribution to be a particularly effective choice for modeling the marginals and the Frank copula to be the best choice for stitching these together into a joint distribution. Our approach offers a significant fidelity improvement and generalizes directly to higher dimensions. In use, this improvement will translate directly to reductions in energy consumption.

Building on the successful applications of Stackelberg Security Games (SSGs) to protect infrastructure, researchers have begun focusing on applying game theory to green security domains such as protection of endangered animals and fish stocks. Previous efforts in these domains optimize defender strategies based on the standard Stackelberg assumption that the adversaries become fully aware of the defender's strategy before taking action. Unfortunately, this assumption is inappropriate since adversaries in green security domains often lack the resources to fully track the defender strategy. This paper (i) introduces Green Security Games (GSGs), a novel game model for green security domains with a generalized Stackelberg assumption; (ii) provides algorithms to plan effective sequential defender strategies --- such planning was absent in previous work; (iii) proposes a novel approach to learn adversary models that further improves defender performance; and (iv) provides detailed experimental analysis of proposed approaches.

In many POMDP applications in computational sustainability, it is important that the computed policy have a simple description, so that it can be easily interpreted by stakeholders and decision makers.  One measure of simplicity for POMDP value functions is the number of alpha-vectors required to represent the value function. Existing POMDP methods seek to optimize the accuracy of the value function, which can require a very large number of alpha-vectors. This paper studies methods that allow the user to explore the tradeoff between the accuracy of the value function and the number of alpha-vectors.  Building on previous point-based POMDP solvers, this paper introduces a new algorithm (alpha-min) that formulates a Mixed Integer Linear Program (MILP) to calculate approximate solutions for finite-horizon POMDP problems with limited numbers of alpha-vectors. At each time-step, alpha-min calculates alpha-vectors to greedily minimize the gap between current upper and lower bounds of the value function. In doing so, good upper and lower bounds are quickly reached allowing a good approximation of the problem with few alpha-vectors . Experimental results show that alpha-min provides good approximate solutions given a fixed number of alpha-vectors on small benchmark problems, on a larger randomly generated problem, as well as on a computational sustainability problem to best manage the endangered Sumatran tiger.

Clustering has gained widespread use, especially for static data. However, the rapid growth of spatio-temporal data from numerous instruments, such as earth-orbiting satellites, has created a need for spatio-temporal clustering methods to extract and monitor dynamic clusters. Dynamic spatio-temporal clustering faces two major challenges: First, the clusters are dynamic and may change in size, shape, and statistical properties over time. Second, numerous spatio-temporal data are incomplete, noisy, heterogeneous, and highly variable (over space and time). We propose a new spatio-temporal data mining paradigm, to autonomously identify dynamic spatio-temporal clusters in the presence of noise and missing data. Our proposed approach is more robust than traditional clustering and image segmentation techniques in the case of dynamic patterns, non-stationary, heterogeneity, and missing data. We demonstrate our method's performance on a real-world application of monitoring in-land water bodies on a global scale.

Many problems in computational sustainability involve constraints on connectivity. When designing a new wildlife corridor, we need it to be geographically connected. When planning the harvest of a forest, we need new areas to harvest to be connected to areas that have already been harvested so we can access them easily. And when town planning, we need to connect new homes to the existing utility infrastructure. To reason about connectivity, we propose a new family of global connectivity constraints. We identify when these constraints can be propagated tractably, and give some efficient, typically linear time propagators for when this is the case. We report results on several benchmark problems which demonstrate the efficiency of our propagation algorithms and the promise offered by reasoning globally about connectivity.

Smart grids enhance power grids by integrating electronic equipment, communication systems and computational tools. In a smart grid, consumers can insert energy into the power grid. We propose a new energy management system (called RLbEMS) that autonomously defines a policy for selling or storing energy surplus in smart homes. This policy is achieved through Batch Reinforcement Learning with historical data about energy prices, energy generation, consumer demand and characteristics of storage systems. In practical problems, RLbEMS has learned good energy selling policies quickly and effectively. We obtained maximum gains of 20.78% and 10.64%, when compared to a Naive-greedy policy, for smart homes located in Brazil and in the USA, respectively. Another important result achieved by RLbEMS was the reduction of about 30% of peak demand, a central desideratum for smart grids.

In this paper, we address the challenge of predicting optimal comfort temperatures of individual users of a smart heating system. At present, such systems use simple models of user comfort when deciding on a set point temperature. These models generally fail to adapt to an individual user’s preferences, resulting in poor estimates of a user’s preferred temperature. To address this issue, we propose a personalised thermal comfort model that uses a Bayesian network to learn and adapt to a user’s individual preferences. Through an empirical evaluation based on the ASHRAE RP-884 data set, we show that our model is consistently 17.5- 23.5% more accurate than current models, regardless of environmental conditions and the type of heating system used. Our model is not limited to a single metric but can also infer information about expected user feedback, optimal comfort temperature and thermal sensitivity at the same time, which can be used to reduce energy used for heating with minimal comfort loss.

In cooperation with a social startup, FoodBank Local, we have been helping Food Bank Australia develop technologies We study an online model of fair division designed to operate more effectively. So far, this has involved building to capture features of a real world charity problem.

Among various traditional art forms, brush stroke drawing is one of the widely used styles in modern computer graphic tools such as GIMP, Photoshop and Painter. In this paper, we develop an AI-aided art authoring (A4) system of non-photorealistic rendering that allows users to automatically generate brush stroke paintings in a specific artist's style. Within the reinforcement learning framework of brush stroke generation proposed, our contribution in this paper is to learn artists' drawing styles from video-captured stroke data by inverse reinforcement learning. Through experiments, we demonstrate that our system can successfully learn artists' styles and render pictures with consistent and smooth brush strokes.