Submodular Function Maximization for Group Elevator Scheduling

We propose a novel approach for group elevator scheduling by formulating it as the maximization of submodular function under a matroid constraint. In particular, we propose to model the total waiting time of passengers using a quadratic Boolean function. The unary and pairwise terms in the function denote the waiting time for single and pairwise allocation of passengers to elevators, respectively. We show that this objective function is submodular. The matroid constraints ensure that every passenger is allocated to exactly one elevator. We use a greedy algorithm to maximize the submodular objective function, and derive provable guarantees on the optimality of the solution. We tested our algorithm using Elevate 8, a commercial-grade elevator simulator that allows simulation with a wide range of elevator settings. We achieve significant improvement over the existing algorithms.Comment: 10 pages; 2017 International Conference on Automated Planning and Scheduling (ICAPS

Learning Dynamical Demand Response Model in Real-Time Pricing Program

Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.Comment: Accepted to IEEE ISGT NA 201

METHOD FOR DATA - DRIVEN LEARNING - BASED CONTROL OF HVAC SYSTEMS USING HIGH - DIMENSIONAL SENSORY OBSERVATIONS

A controller for controlling an operation of an air - conditioning system conditioning an indoor space includes a data input to receive state data of the space at multiple points in the space , a memory to store a code of a reinforcement learning algorithm and a history of the state data and a history of control commands having been applied to the air - conditioning system , wherein the history of the control commands is associated with the state data and history of rewards , a processor coupled to the memory determines a value function outputting a cumulative value of the rewards and transmits a control command by using the reinforcement learning algorithm , and a data output to receive the control command from the processor and transmit a control signal to the air - conditioning system , wherein the control signal controls at least one actuator of the air - conditioning system according to the control command

Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning

In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components from it. We revisit the classical formulation of rigid body dynamics in order to extrapolate the physical dynamical components, such as inertial and gravitational components, from an inverse dynamics model. After estimating the dynamical components, the forward dynamics can be computed in closed form as a function of the learned inverse dynamics. We tested the proposed method with several machine learning models based on Gaussian Process Regression and compared them with the standard approach of learning the forward dynamics directly. Results on two simulated robotic manipulators, a PANDA Franka Emika and a UR10, show the effectiveness of the proposed method in learning the forward dynamics, both in terms of accuracy as well as in opening the possibility of using more structured~models