This research reviews reinforcement learning for optimizing building energy management.
problem Optimizing energy utilization in building management systems.
method Comprehensive review of reinforcement learning applications in building energy management.
result Challenges and future directions in reinforcement learning for building energy management.
The paper tackles energy management in buildings with PCM using dynamic programming.
problem Optimal scheduling of HVAC systems in buildings with PCM is challenging due to nonlinear and non-convex characteristics.
method The paper uses dynamic programming to address the nonlinear nature of PCM, incorporating macro actions and multi-time scale Markov decision processes to reduce computational burden.
result The proposed method demonstrates a computational speed-up of up to 12,900 times compared to direct DP application.
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
problem Mitigating energy consumption in commercial buildings through occupant plugload control.
method Field experiments with visual feedback and monetary incentives in government and university buildings.
result Mean energy reduction of ~9.52% in office environments and ~21.61% in university environments with visual feedback.
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we pr…
New deep learning model optimizes energy use in buildings.
problem Optimizing energy use and comfort in large buildings.
method Transformer-based metamodel trained with simulation and sensor data, calibrated with CMA-ES, optimized with multi-objective algorithms.
result Optimal settings reduce energy loads while maintaining thermal comfort and air quality.
Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.
problem Improving energy efficiency in smart building infrastructure through occupant behavior.
method Introduces a novel game-theoretic framework with human interaction, incorporating utility learning and deep neural networks.
result Demonstrates highly accurate prediction of occupant energy resource usage and explainable decision-making.
CESAR improves wind speed and power forecasting for high-resolution simulations.
problem Accurate high-resolution wind forecasting for efficient power grid management.
method A spatio-temporal neural network model using deep convolutional autoencoder and echo state network.
result CESAR provides up to 17% improvement in wind speed and power forecasting compared to best alternatives.
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy …
A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…
Proposes ML-RBM for non-intrusive load monitoring without appliance-level data.
problem Non-intrusive load monitoring without appliance-level data.
method Multi-label Restricted Boltzmann Machine (ML-RBM)
result Experimental evaluation of proposed and state-of-the-art techniques.
A brisk building boom of hydropower mega-dams is underway from China to Brazil. Whether benefits of new dams will outweigh costs remains unresolved despite contentious debates. We investigate this question with the "outside view" or "reference class forecasting" based on literature on decision-making under uncertainty …
Physics-based framework improves building energy forecasting.
problem Lack of physical correspondence in machine learning models for building energy systems.
method Combines LTI SSMs with subspace-based domain adaptation (SDA).
result Physics-derived subspaces align with data-derived subspaces for better forecasting.
LAD-BNet improves real-time energy forecasting on edge devices.
problem Real-time energy forecasting on edge devices for smart grid optimization and intelligent buildings.
method Hybrid neural architecture combining temporal lag exploitation and TCN with dilated convolutions.
result 14.49% MAPE at 1-hour horizon with 18ms inference time on Edge TPU, 8-12x faster than CPU.
Study predicts room occupancy using machine learning, achieving high accuracy.
problem Efficient room occupancy management for energy conservation.
method Leveraged diverse machine learning models, focusing on Random Forest.
result Random Forest model achieved highest predictive accuracy.
CityTFT models urban building energy using a data-driven approach.
problem Current UBEM methods are time-consuming and based on physics.
method CityTFT uses a TFT framework with an augmented loss function.
result CityTFT predicts energy demands with high accuracy.
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or c…
Deep RL agent secures 2nd place in CityLearn Challenge for district demand management.
problem Optimizing electrical demand of diverse buildings in a district.
method Centralised 'Soft Actor Critic' deep reinforcement learning agent.
result Achieved an averaged score of 0.967 on challenge dataset.
Electricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient an…
Advances in renewable energy generation and introduction of the government targets to improve energy efficiency gave rise to a concept of a Zero Energy Building (ZEB). A ZEB is a building whose net energy usage over a year is zero, i.e., its energy use is not larger than its overall renewables generation. A collection …
Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.
problem Optimizing energy and reserve allocation between zones to minimize costs and ensure deliverability.
method Developed allocation models for co-allocating traded energy and reserve products, considering both deterministic and stochastic flows.
result Excess reserve supplies can be used for congestion management, leading to additional network benefits.
The paper optimizes air conditioning setpoints using machine learning.
problem Static setpoints waste energy and increase costs.
method Deep-learning model based on RNNs for predicting future temperatures.
result RNNs outperform state-of-the-art models in prediction accuracy.
Improved non-intrusive load monitoring with a novel neural network.
problem Accurately disaggregating household electricity consumption without dedicated meters.
method Developed a scale- and context-aware network with multi-scale features and contextual information.
result Significantly improved accuracy compared to state-of-the-art methods.
Versatile model for High Energy Physics events.
problem Modeling complex interactions in high-energy physics data.
method Energy-based probabilistic model with multi-purpose architecture.
result Achieves success in diverse applications like simulation, anomaly detection, and particle identification.
Paper adapts causal analysis for time-dependent systems, especially energy management.
problem Challenges in root-cause analysis for systems with lagged time-dependencies, particularly in energy management.
method Adapts causal root-cause analysis method to time-dependent systems, discusses two truncation approaches.
result Extension effectively localizes root-causes in feature and time domain with enough lags.
Adversarial attacks degrade DRL-based EV energy management systems.
problem Adversarial attacks on DRL-based energy management systems of electric vehicles.
method Generated adversarial examples to degrade DRL performance using low-dimensional state representations.
result Adversarial attacks can significantly degrade DRL-based EV energy management systems.
This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
Deep learning boosts building energy load forecasting.
problem Short-term load forecasting in buildings.
method Stacked Boosters Network architecture with sparse interactions, parameter sharing, and equivariant representations.
result Outperforms state-of-the-art models in short-term load forecasting tasks.
This research optimizes energy consumption forecasting in Puno using parallel computing and ARIMA models.
problem Improving energy consumption forecasting accuracy and efficiency in Puno.
method Parallel computing and ARIMA models for forecasting energy consumption.
result Notable improvements in computational efficiency and data processing capabilities.
This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of…
Deep learning adapts HVAC models to new buildings.
problem Adapting thermal dynamics models to new buildings with limited data.
method Deep supervised domain adaptation (DSDA) using LSTM-based Sequence to Sequence model.
result Deep supervised domain adaptation improves predictive performance over learning from scratch.
RL agent learns to save costs by managing household energy storage.
problem Maximizing cost savings in smart grids with household energy storage.
method Data-driven RL agent learns from tariff structures and storage capacity.
result RL agent explains its learning process and strategies.
In this paper, we analyze energy-harvesting adaptive diffusion networks for a distributed estimation problem. In order to wisely manage the available energy resources, we propose a scheme where a censoring algorithm is jointly applied over the diffusion strategy. An energy-aware variation of a diffusion algorithm is us…
Grid-scale batteries' bid patterns in price uncertainty markets
problem Interpreting bids from grid-scale batteries in wholesale electricity markets under price uncertainty
method Developing an asset-level model of a price-taking battery
result Empirical results deliver insights into withholding behavior, uncertainty effects, and risk management reshaping bid curves
Proposes a framework for energy-efficient AIGC workload scheduling in cloud data centers.
problem Challenges of scheduling AIGC workloads for energy efficiency and quality control.
method Joint energy management and coordinated AIGC workload scheduling framework with diffusion model-aided reward shaping.
result Effective learning of scheduling policies under sparse environmental feedback.
New algorithm controls large groups of devices to match energy demand signals.
problem Controlling large populations of electrical devices to match energy demand signals.
method Developed MD-MFC algorithm for finite horizon Markovian mean field control problem.
result MD-MFC provides theoretical guarantees for convex and Lipschitz objective functions.
Deep Autoencoder outperforms in anomaly detection for building energy data.
problem Automated detection of faulty data in learning applications.
method Training and comparison of Simple, Deep, and Supervised Deep Autoencoders on ASHRAE building energy dataset.
result Supervised Deep Autoencoder outperforms in total anomalies detected.
Proposes a pricing agent using reinforcement learning to balance renewable energy demand.
problem Intermittent renewable energy sources challenge carbon-free electricity generation.
method Reinforcement learning approach to balance customer demand with renewable energy generation.
result Demonstrates improved electricity pricing strategy for renewable energy integration.
Ensemble method for fast portfolio valuation and risk management.
problem Dynamic portfolio valuation and risk management from cash flow data.
method Regression trees for dynamic value process learning.
result Fast and accurate estimator with closed-form solution.
The paper analyzes portfolio management in the Heston model, proposing new strategies.
problem Investment performance influenced by asset diversity and cash inclusion.
method Monte Carlo simulations in the Heston model, MACD and RSI technical analysis.
result New portfolio management strategies based on MACD and RSI.
Paper uses news data to model asset correlations without market data.
problem Traditional risk models rely on market data; this paper offers an alternative.
method Uses encoder-only language models to embed news data, then calculates asset return distributions and covariance through Energy Distance.
result Established connections between distributional differences and excess returns co-movements using Energy Distance.
Machine learning with kernels for portfolio valuation and risk management.
problem Dynamic portfolio valuation and risk management in finance.
method Machine learning with kernels to learn the dynamic value process of a portfolio from cumulative cash flow data.
result Asymptotic consistency and finite sample error bounds demonstrated for finance applications.
Smart grid uses deep learning to optimize household energy use.
problem Optimizing household energy use under real-time pricing schemes.
method Multi-agent deep actor-critic learning for decentralized agents with partial observability.
result Deep reinforcement learning reduces peak-to-average energy consumption and costs.
A DRL approach optimizes resource allocation in BFL to reduce latency and energy consumption.
problem Energy and CPU constraints of mobile devices and increased training latency due to blockchain mining.
method Deep Reinforcement Learning (DRL) to derive optimal decisions for MLMO.
result Optimal resource allocation and block generation rate to minimize system latency, energy consumption, and incentive cost.
New pricing framework allocates costs of operating reserves and transmission.
problem Allocating costs of operating reserves and transmission efficiently.
method Causation-based framework using contingency-constrained scheduling models.
result More comprehensive and efficient cost-reflective market operations.
Bayesian deep learning improves building energy simulation accuracy.
problem Uncertainty in surrogate models for building energy performance.
method Training dropout neural networks and stochastic variational Gaussian Processes.
result Surrogate models reduce errors by up to 30% with uncertainty-aware sampling.
In this paper we look at the efficacy of different risk measures on energy markets and across several different stock market indices. We use both the Value at Risk and the Tail Conditional Expectation on each of these data sets. We also consider several different durations and levels for historical risk measures. Throu…
New estimates for Hitchin's equations at high energy.
problem Solutions to Hitchin's self-duality equations at high energy.
method New estimates and asymptotic decoupling phenomenon.
result Generalization to arbitrary Higgs bundles.
This paper provides a ML framework for diabetes prediction and care management.
problem Diabetes prediction and care management challenges in real-world healthcare.
method Illustrates a Machine Learning framework for T2DM prediction and risk stratification.
result ML models align with physician's disease management steps.