Research calculates elasticities of energy demand in Ecuador, finding it highly income elastic.
problem Analyzing energy demand elasticities in Ecuador to inform policy.
method Cointegration analysis and Dynamic Ordinary Least Squares approach with structural breaks.
result Energy demand in Ecuador is highly income elastic, with no price elasticity and inverse relationship with industrial production.
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.
Dynamic pricing aims to match power supply and demand in an energy transition.
problem Mismatch between renewable energy supply and consumer demand.
method Formalizes decision-making problem, designs forecasting models, and statistical demand response models.
result Dynamic pricing can synchronise power supply and demand effectively.
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.
The paper proposes using energy disaggregation techniques to detect flexible demand in buildings.
problem Real-time detection of flexible demand in buildings.
method The paper investigates the use of existing classification methods and proposes the use of Restricted Boltzmann Machine for feature extraction.
result The proposed approach shows robustness and good generalization capabilities with at least 96% accuracy.
Paper uses online learning to disaggregate demand from distribution feeder data.
problem Lack of real-time information on distributed energy resources.
method Dynamic Fixed Share (DFS) online learning algorithm using historical data.
result DFS effectively disaggregates demand from distribution feeder measurements.
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.
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.
UK imports face higher volatility during high demand periods.
problem Price volatility and national sovereignty during high demand.
method Analysis of early 2017 conditions in France, Germany, and UK.
result National interests take precedence over environmental sustainability.
Bayesian algorithm learns consumer preferences for energy-saving home automation.
problem Effective energy saving for residential consumers in real-time tariffs.
method Bayesian learning algorithm to estimate comfort level from appliance use history.
result Algorithm outperforms regression analysis in numeric experiments with simulated consumer behavior.
Paper optimizes demand aggregation for low-level electricity markets.
problem Accurate short-term load forecasting at low aggregation levels for market participants.
method Probabilistic portfolio optimization of residential households' demand using ARMA-GARCH models or KDE forecasts.
result Seasonal Residual approach outperforms others in accuracy and efficiency.
Deep learning predicts energy loads and prices with LSTM and EVT.
problem Predicting extreme loads in energy grids due to supply and demand fluctuations.
method Deep spatio-temporal models and EVT for tail behavior of load spikes.
result Deep LSTM models outperform traditional methods in capturing nonlinearities.
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.
Paper presents a method for probabilistic load forecasting using adaptive online learning.
problem Inability to assess intrinsic uncertainties and capture dynamic changes in consumption patterns.
method Adaptive online learning of hidden Markov models for recursive parameter updates and sequential prediction.
result Significant improvement in performance compared to existing techniques across various scenarios.
SEA model predicts heat demand combining neural network and ARIMA.
problem Predicting heat demand with periodicity.
method Combining Elman neural network and ARIMA models for seasonal and trend predictions.
result SEA model shows promising performance in heat demand prediction.
Token economics improves energy systems with incentives and efficiency.
problem Traditional energy systems have inefficiencies and lack incentives.
method Integrating token economy and blockchain technology.
result Token economic systems enhance energy efficiency and reduce emissions.
Paper presents a shape-based approach for better household load curve clustering and prediction.
problem Difficulty in classifying and predicting consumer energy consumption due to many clusters.
method Shape-based approach using Dynamic Time Warping (DTW) to align energy consumption patterns.
result Reduces the number of representative groups by 50% and improves prediction accuracy.
The paper compares machine learning models for forecasting residential gas demand, highlighting the impact of temperature forecasts.
problem Forecasting residential gas demand for optimal energy planning.
method Implemented and compared five models: Ridge Regression, GP, k-Nearest Neighbour, ANN, and Torus Model.
result ANN is the best model in terms of RMSE, while GP is the best in terms of MAE.
STOIC improves energy demand forecasting with reliable uncertainty estimates.
problem Accurate point forecasts alone are insufficient for energy systems; reliable uncertainty estimates are needed.
method Integrates graph-based forecasting with tabular foundation models for zero-shot calibration of spatial-temporal residuals.
result STOIC delivers more reliable and robust uncertainty estimates for complex graph-structured energy time series.
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.
The paper proposes a new DR model to better predict EUCs' responses in real-time pricing.
problem Static demand functions fail to capture temporal correlation in EUC behaviors.
method Proposes a dynamical DR model using neural networks to learn from historical data.
result The dynamical DR model significantly outperforms static models in predicting EUC responses.
Generative model predicts daily activity sequences with duration-aware dynamics.
problem Accurately forecasting granular daily activity sequences for energy demand.
method Hierarchical semi-Markov models with duration-aware dynamics.
result Explicitly modeling activity durations improves predictive performance.
Paper uses STPN for energy/power prediction in complex systems.
problem Predicting energy in complex dynamical systems.
method Spatiotemporal pattern network (STPN) framework with mutual information metric.
result Improved energy prediction accuracy in wind and residential energy contexts.
New model predicts energy prices under different scenarios.
problem Complex causal relationships in energy markets with continuous regime changes.
method Augmented Time Series Structural Causal Models (ATSCM) integrating neural causal discovery.
result Enables novel counterfactual queries in energy markets.
Study shows different types of volatility shocks impact oil markets differently.
problem Understanding the impact of volatility shocks on oil markets.
method Novel frequency domain methodology applied to crude oil and its derivatives.
result Shocks to volatility with shorter than one week are increasingly important.
EDCompress optimizes energy efficiency of CNN models on edge devices.
problem Low energy consumption for edge devices with diverse dataflow types.
method Energy-aware model compression using reinforcement learning.
result Improves energy efficiency by 20X, 17X, 37X in various networks.
Proposes DMVST-Net for taxi demand prediction.
problem Improving taxi demand prediction for smart city resource allocation.
method Deep Multi-View Spatial-Temporal Network (DMVST-Net) combining LSTM, CNN, and semantic views.
result Demonstrates effectiveness over state-of-the-art methods on large-scale taxi demand data.
Deep neural network improves NILM with attention mechanism.
problem Energy disaggregation of individual appliance power demands from aggregate meter readings.
method Regression and classification subnetworks with attention mechanism.
result Proposed model outperforms state-of-the-art on REDD and UK-DALE datasets.
Paper analyzes how weather data improves solar power prediction.
problem Predicting the unpredictability of solar power generation.
method Examined the impact of weather data on photovoltaic power prediction.
result Weather data significantly improves photovoltaic power prediction accuracy.
This paper proposes a joint energy and data market to handle uncertainty in energy procurement.
problem Handling uncertainty in energy markets through data markets.
method Modeling a day-ahead retailer energy procurement problem with uncertain demand, integrating forecasting and optimisation, and using differential privacy.
result The value of joint energy and data clearing is highlighted through numerical case studies.
Method minimizes electricity procurement cost based on demand prediction errors.
problem Minimizing electricity procurement cost in spot markets.
method Formulate method to minimize procurement cost over two parameters.
result Minimizes total electricity cost with known unit prices and prediction errors.
Study assesses forecasting models in uncertain weather data.
problem Performance of forecasting models in uncertain weather data.
method Selected influential predictors, analyzed uncertainty, trained model using observed data.
result Comparison of forecasting methods in solar PV generation.
Paper introduces a neural framework for accurate energy forecasting.
problem Challenges of forecasting energy demand and supply due to variability of renewable sources and dynamic consumption patterns.
method Integrates Neural ODEs, graph attention, multi-resolution wavelet transformations, and adaptive learning of frequencies.
result Consistently outperforms state-of-the-art baselines in various forecasting metrics across diverse datasets.
Model forecasts water demand with probabilistic multi-step-ahead approach.
problem Accurate probabilistic forecasts of water demand for operational control.
method Time series model with Lasso for high-dimensional feature space.
result Accurate, interpretable, and fast computable forecasting model.
Paper presents fast methods for pricing energy derivatives using mean-reverting jump-diffusion models.
problem Pricing energy derivatives with mean-reverting and occasional spikes.
method Exact and fast simulation of spot price dynamics using Ornstein-Uhlenbeck and jump-diffusion processes.
result Apparent computational advantages of the proposed procedures for pricing Asian options, gas storages, and swings.
The paper forecasts joint electricity demand across 14 British regions using additive models.
problem Forecasting regional electricity demand with cross-regional dependencies.
method Modified Cholesky parametrisation for multivariate Gaussian model, gradient boosting for model selection.
result The proposed model outperforms non-Gaussian copula-based models in forecasting.
Deep learning models combine text and time-series data for better taxi demand forecasts.
problem Accurate taxi demand forecasting in event areas.
method Two deep learning architectures using word embeddings, convolutional layers, and attention mechanisms.
result The models significantly reduce forecast error by fusing text and time-series data.
This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.
problem The impact of spatial and temporal resolution on energy system models.
method Global sensitivity analysis to compare structural aspects, spatial, and temporal resolution.
result Temporal resolution has a higher influence on all results parameters compared to spatial resolution.
A new method using energy distance for ensemble and scenario reduction.
problem Solving complex dynamic and stochastic programs, especially in energy systems.
method Proposes a new method based on energy distance for ensemble and scenario reduction.
result Reduced scenario sets exhibit better statistical properties for energy distance than Wasserstein distance.
A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
problem Day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
method Online forecast combination of multiple point prediction models with a holiday adjustment procedure and smoothed Bernstein Online Aggregation (BOA).
result Excellent forecasting performance, particularly due to the holiday adjustment procedure and fully adaptive smoothed BOA approach.
Active sensing improves energy breakdown without installing sensors.
problem Reduce energy consumption by providing appliance-level energy breakdown.
method Active learning solution based on low-rank tensor completion.
result Our approach gives better performance with fewer sensors installed.
Study optimizes building energy control and power planning using RL.
problem Optimizing academic buildings' HVAC and power systems.
method Reinforcement Learning (RL) for scheduling and planning.
result Algorithm optimizes hourly energy usage and handles short-term changes.
Study on energy storage's impact on electricity prices and profitability.
problem Analyzing the profitability of energy storage in electricity markets.
method Characterized optimal operating strategy for storage systems, determined equilibrium price in a market with storage, renewables, and conventional producers, and characterized price process using stochastic differential equations.
result Increased average revenues and interquantile ranges for storage assets in energy transition scenarios.
Paper models uncertainty in electricity and gas markets to assess its impact.
problem Addressing uncertainties in coupled electricity and gas markets.
method Integrated and stochastic optimisation approaches for large-scale energy systems.
result Quantifies the value of encoding uncertainty in models.
MF-PID uses interacting samples to efficiently transport probability mass.
problem Efficiently transporting probability mass in generative models.
method Introducing Mean-Field Path-Integral Diffusion (MF-PID) where samples become interacting agents.
result MF-PID achieves 19-24% reductions in control energy for demand-response control of energy systems.
This paper analyzes energy and carbon footprints in distributed and federated learning.
problem High energy costs and carbon emissions in centralized AI methods.
method A novel framework quantifying energy and carbon footprints in vanilla and consensus-based FL methods.
result Optimal bounds and operational points for green FL designs and sustainability assessment.
FPDeep accelerates CNN training on FPGA clusters with high parallelism and energy efficiency.
problem Scaling DNN training to large clusters with high utilization and balanced workload.
method Hybrid model and layer parallelism, fine-grained pipeline, balanced workload partitioning.
result FPDeep achieves high parallelism and utilization, reducing storage demand to on-chip memory.
Wavelet analysis reveals financialization effects on oil-food price correlation.
problem Investigating the correlation between oil and food prices and their determinants.
method Wavelet analysis and energy-based measures to differentiate high and low frequency movements.
result Significant local correlation between food and oil is due to financialization and emerging economies' demand.