Competency questions help experts select best clustering for energy data.
problem Ad hoc and subjective selection of clustering structures by domain experts.
method Formalize expert knowledge and requirements with competency questions.
result Competency questions improve reproducibility and evaluation of clustering applications.
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.
problem Accurate hourly forecasting of residential heating and electricity demand.
method Probabilistic deep learning models trained on gas-heated region data.
result Significant improvement in forecast accuracy compared to NREL's ResStock model.
Crowdsourcing generated useful hypotheses for predicting residential energy usage.
problem Predicting residential electric energy usage using crowdsourced data.
method Crowdsourced questions and answers were used to build a predictive model of monthly electric energy consumption.
result The crowd can generate useful hypotheses for predicting energy usage, even with sparse data.
Graph neural networks improve residential location choice predictions.
problem Capturing spatial dependence in discrete choice models.
method Graph Neural Networks (GNN) for analyzing spatial alternatives.
result GNN-DCMs outperform classical models in residential location choice predictions.
The estimate of a Multiperiod probability of default applied to residential mortgages can be obtained using the mean of the observed default, so called the Mean of ratios estimator, or aggregating the default and the issued mortgages and computing the ratio of their sum, that is the Ratio of means. This work studies th…
New method for selecting clusters in residential electricity data.
problem Selecting useful clusters in electricity consumption data.
method Formalizing expert knowledge as external validation measures.
result Successfully reconstructed customer archetypes.
This paper analyzes load predictability at different aggregation levels and improves forecasting accuracy.
problem Challenges in short-term load forecasting, especially at low aggregation levels.
method Characterized SME and residential loads, quantified predictability using approximate entropy, compared various STLF techniques.
result Improved forecasting accuracy for low-aggregation loads, validated with data processing techniques.
We have analyzed the risks of possible development of bubbles in the Swiss residential real estate market. The data employed in this work has been collected by comparis.ch, and carefully cleaned from duplicate records through a procedure based on supervised machine learning methods. The study uses the log periodic powe…
Speculative bubbles have been occurring periodically in local or global real estate markets and are considered a potential cause of economic crises. In this context, the detection of explosive behaviors in the financial market and the implementation of early warning diagnosis tests are of critical importance. The recen…
In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfor…
Study forecasts Turkish residential NGD using JITL-GPR, reducing errors.
problem Accurately predict future monthly NGD for Turkey's import contracts.
method Used historical NG consumption data, applied various time series models, and introduced JITL-GPR.
result JITL-GPR reduces forecast errors compared to traditional methods.
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.
I studied what role the US stock markets and money markets have possibly played in the Gross Private Domestic Investment (GPDI) of the United States from the year 1959 to the year 2001, Gross Private Domestic Investment refers to the total amount of investment spending by businesses and firms located within the borders…
Model forecasts natural gas consumption with Fourier series and feedback.
problem Forecast natural gas consumption for risk minimization.
method Modulated Fourier series with temperature deviations, day-ahead feedback.
result Model outperforms time series methods for long-term projections.
Real Estate Investment Trusts (REITs) are the only truly liquid assets related to real estate investments. We study the behavior of U.S. REITs over the past three decades and document their return characteristics. REITs have somewhat less market risk than equity; their betas against a broad market index average about .…
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.
Paper presents an energy-efficient RL method for sensor networks.
problem Energy consumption in sensor networks for health monitoring.
method Adaptive Reinforcement Learning framework using SARSA algorithm.
result Achieves performance enhancement and energy savings over time.
When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamina…
Framework optimizes PV-battery investment timing to maximize value.
problem Optimizing investments in residential PV-battery systems under uncertain market conditions.
method Real options valuation framework with multi-stage compound options, incorporating Monte Carlo simulation.
result Optimal timing of PV-battery investment increases overall value.
Study improves loan default risk estimation using advanced regression models.
problem Modeling loan default risk over time is challenging and affects financial reserves.
method Comparative study of three multistate regression techniques: Markov chain, beta regression, and multinomial logistic regression.
result Each successive model outperforms the previous, indicating greater sophistication.
Proposes method for global explanations of credit risk models.
problem Lack of interpretability in credit risk scoring models.
method Sampling decision function to learn interpretable models.
result Unified solution to approximate complex decision boundaries.
As economic entities become increasingly interconnected, a shock in a financial network can provoke significant cascading failures throughout the system. To study the systemic risk of financial systems, we create a bi-partite banking network model composed of banks and bank assets and propose a cascading failure model …
Improved gas demand forecasting using ensemble methods.
problem Short-term prediction of gas demand components.
method Nine base forecasters (Ridge Regression, GP, NN, ANN, Torus, LASSO, Elastic Net, RF, SVR) and four ensemble predictors (simple, weighted, subset, SVR aggregation) were evaluated.
result Ensemble predictors outperformed individual base forecasters and TSO predictions.
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
Credit expansion led to stronger household leverage cycles during the U.S. business cycle.
problem Understanding the role of credit supply in the U.S. business cycle.
method Causal evidence from 1999-2010 U.S. business cycle data.
result Credit expansion, particularly in private-label mortgages, caused stronger household leverage cycles.
Deep learning classifies land use from high-resolution aerial imagery.
problem Variations in land features in aerial imagery due to sensor settings and context.
method Used deep convolutional neural networks to classify land use from VHR orthophoto mosaics.
result Deep learning can accurately classify land use from high-resolution visible band multispectral imagery.
Proposes statistical inference for L2-Boosting.
problem Statistical inference for L2-Boosting. method Post-selection inference for iterative variable selection in L2-Boosting. result Developed tests and confidence intervals for L2-Boosting. There is a widely-accepted need to revise current forms of health-care provision, with particular interest in sensing systems in the home. Given a multiple-modality sensor platform with heterogeneous network connectivity, as is under development in the Sensor Platform for HEalthcare in Residential Environment (SPHERE) …
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.
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution gri…
New algorithms improve signal processing in federated learning.
problem Efficiently process distributed signal samples with privacy and communication constraints.
method Proposes overpredictive signal approximations using convex optimization.
result Quantifies tradeoffs between communication cost, sampling rate, and approximation error.
Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic…
Transfer learning framework for fragility modeling under domain shift and class imbalance
problem Data gaps in structural fragility modeling
method Transfer learning
result Improves failure detection and predictive stability in low-data regimes
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.
Optimizes loan recovery timing by forecasting cash flows.
problem Minimizing overall credit loss in loan portfolios.
method Forecast future cash flows using probabilistic and Markov chain models.
result Empirical illustration of loss-optimal recovery timing.
New method for clustering tasks with heterogeneous data.
problem Clustered multitask learning with semiparametric and heterogeneous nuisances.
method Adaptive fused orthogonal estimator with Neyman-orthogonal losses and data-driven fusion penalties.
result Achieves exact clustering recovery and pooled parametric convergence rates.
The study explores machine learning for predicting customer propensity-to-pay uncertainty.
problem Improving customer experience, reducing financial hardship, and managing cash flow risks.
method Investigated machine learning models for predicting propensity-to-pay, focusing on uncertainty estimation.
result Novel Bayesian Neural Network model for binary classification of propensity-to-pay.
Hierarchical clustering models capture electricity load patterns from smart meters.
problem Improving efficiency and sustainability of electricity systems.
method Quantile autocovariances and autocorrelations for summarizing time series.
result Clusters identify relevant consumption behaviors and capture geo-demographic segmentation.
Statistical learning improves reactive power control in distribution systems.
problem Challenges in reactive power control due to renewable energy sources and flexible loads.
method A deep neural network parameterizes the input-output relationship between grid states and optimal reactive power control. Unknown weights are learned offline to minimize power loss, and inference is fast with matrix-vector multiplications.
result Computational efficiency and robustness to random input perturbations demonstrated in a 47-bus distribution network.
Deep learning model forecasts PV power production with high accuracy.
problem Accurate forecasting of photovoltaic power production is needed for electricity infrastructure.
method Sequence to sequence model with attention mechanism using weather predictions and historical data.
result The model outperforms existing methods in forecast skill score.
A censored transformed model for proportional outcomes with boundary mass and an application to loss given default modeling.
problem Modeling proportional outcomes with boundary mass in loss given default (LGD) modeling.
method Zero-one censored transformed normal (ZOC-TN) model.
result Captures a wider range of qualitative density shapes than benchmark models while being parsimonious, computationally efficient, and numerically stable.
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.
The paper uses GIS data to predict urban sprawl.
problem Overgrowth and expansion of low-density areas with car dependency and segregation.
method Data mining algorithms (Apriori, J4.8) adapted for geospatial analysis using ArcGIS.
result Prototype spatial decision support system (SDSS) predicts urban sprawl and estimates impact variables.
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.
We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, t…
A new knockoff statistic using conditional prediction function improves variable selection in complex models.
problem Controlling false discovery rate in complex models with nonlinear relationships.
method Introducing a knockoff statistic based on the conditional prediction function for use with machine learning models.
result The CPF statistics provide superior power in detecting prognostic variables over existing knockoff statistics.
Novel approach models life events using causal discovery and survival analysis.
problem Modeling life event choices and occurrence from a probabilistic perspective.
method Bi-level problem formulation: causal discovery for life events graph, survival analysis for time-to-event modeling.
result Identification of causal relationships and factors influencing transition rates between life events.
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.