Modeling and forecasting building energy consumption: A review of data-driven techniques

M Bourdeau, X qiang Zhai, E Nefzaoui, X Guo… - Sustainable Cities and …, 2019 - Elsevier
Building energy consumption modeling and forecasting is essential to address buildings
energy efficiency problems and take up current challenges of human comfort, urbanization …

Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects

NE Benti, MD Chaka, AG Semie - Sustainability, 2023 - mdpi.com
This article presents a review of current advances and prospects in the field of forecasting
renewable energy generation using machine learning (ML) and deep learning (DL) …

Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study

VK Saini, R Kumar, AS Al-Sumaiti, A Sujil… - Electric Power Systems …, 2023 - Elsevier
This paper provides an extensive review of learning-based short-term forecasting models for
smart grid applications. In addition to this, the paper also explores forecasting models …

[HTML][HTML] Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning

E Mocanu, PH Nguyen, WL Kling, M Gibescu - Energy and Buildings, 2016 - Elsevier
Abstract In a future Smart Grid context, increasing challenges in managing the stochastic
local energy supply and demand are expected. This increased the need of more accurate …

Adaptive optimal control of continuous-time nonlinear affine systems via hybrid iteration

O Qasem, W Gao, KG Vamvoudakis - Automatica, 2023 - Elsevier
In this paper, a novel successive approximation framework, named hybrid iteration (HI), is
proposed to fill up the performance gap between two well-known dynamic programming …

Experience replay-based deep reinforcement learning for dialogue management optimisation

S Malviya, P Kumar, S Namasudra… - Transactions on Asian and …, 2022 - dl.acm.org
Dialogue policy is a crucial component in task-oriented Spoken Dialogue Systems (SDSs).
As a decision function, it takes the current dialogue state as input and generates appropriate …

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning

MB Radac, RE Precup, RC Roman - ISA transactions, 2018 - Elsevier
This paper proposes a combined Virtual Reference Feedback Tuning–Q-learning model-
free control approach, which tunes nonlinear static state feedback controllers to achieve …

Adaptive optimal output regulation of time-delay systems via measurement feedback

W Gao, ZP Jiang - IEEE transactions on neural networks and …, 2018 - ieeexplore.ieee.org
This brief proposes a novel solution to problems related to the measurement feedback
adaptive optimal output regulation of discrete-time linear systems with input time-delay …

Reinforcement learning based overtaking decision-making for highway autonomous driving

X Li, X Xu, L Zuo - 2015 Sixth International Conference on …, 2015 - ieeexplore.ieee.org
In this paper, we develop an intelligent overtaking decision-making method for highway
autonomous driving. The key idea is to use reinforcement learning algorithms to learn an …

A survey on model-based mission planning and execution for autonomous spacecraft

M Tipaldi, L Glielmo - IEEE Systems Journal, 2017 - ieeexplore.ieee.org
Different drivers are nowadays leading spacecraft toward an increased level of on-board
autonomy. In this paper, we survey model-based techniques as a vehicle to implement …