Disaggregation Method Research Articles

A Disaggregation Method for Estimation of Adult Competency in Small Domains

Estimation at fine levels of aggregation is necessary, to better describe society. Small area estimation model-based approaches that combine sparse survey data with rich data from auxiliary sources have been proven useful to improve the reliability of estimates for small domains. Considered here is a scenario where small area model-based estimates, produced at a given aggregation level, need to be disaggregated to better describe the social structure at finer levels. For this scenario, an allocation method was developed to implement the disaggregation, overcoming challenges associated with data availability and model development at such fine levels. The method is applied to adult literacy and numeracy estimation at the county-by-group-level, using data from the U.S. Program for the International Assessment of Adult Competencies. In this application, the groups are defined in terms of age or education.

Synergistic Non-Intrusive Load Monitoring: Dual-Model Training and Inference for Improved Load Disaggregation Prediction

Load disaggregation is the process of identifying an individual appliance’s power demand within aggregate electrical load data. Virtually all recently proposed disaggregation methods are based on neural networks, thanks to their superior performance. Their achievable accuracy is, however, often limited by the quality of the data that have been used to train the underlying neural networks. In particular, if electrical appliances exhibit vastly differing temporal schedules and operational modes, their heterogeneous power consumption data can poison the disaggregation models and thus lead to a degraded overall accuracy. This paper presents a novel load disaggregation approach that relies on the use of multiple disaggregation models. We use a clustering method to partition training data by their power consumption patterns, allowing our approach to train separate models for appliance data exhibiting similar electricity consumption patterns. During disaggregation, our scheme selects the model that is closest to the appliance’s power consumption pattern, thereby leveraging the most fitting model for each specific instance. We demonstrate how our method surpasses the accuracy of the individual models designated for each cluster, while simultaneously improving upon the baseline model’s performance. When applied to the widely used ECO dataset, our approach achieves an average improvement of 13% in disaggregation accuracy over the use of a single baseline model.

NASA Earth Exchange Downscaled Climate Projections 30 Arcseconds CMIP6

We describe the latest version of the NASA Earth eXchange Downscaled Climate Projections 30 arcseconds (NEX-DCP30-CMIP6). The archive contains downscaled historical and future projections for 1950–2100 based on output from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The downscaled products were produced using a daily variant of the monthly bias correction/spatial disaggregation (BCSD) method and are at 30-arcsecond horizontal resolution. Four variables (maximum temperature, minimum temperature, precipitation, and vapour pressure) from five CMIP6 experiments (historical, and the four Tier 1 scenarios: SSP1.2-6, SSP2.4-5, SSP3.7-0, and SSP5.8-5) are provided as available from thirty global climate models. The downscaled data cover the coterminous United States (CONUS), extending from approximately 24-50 degrees North and 235–294 degrees East.

Assessing the Effect of Bias Correction Methods on the Development of Intensity–Duration–Frequency Curves Based on Projections from the CORDEX Central America GCM-RCM Multimodel-Ensemble

This work aims to examine the effect of bias correction (BC) methods on the development of Intensity–Duration–Frequency (IDF) curves under climate change at multiple temporal scales. Daily outputs from a 9-member CORDEX-CA GCM-RCM multi-model ensemble (MME) under RCP 8.5 were used to represent future precipitation. Two stationary BC methods, empirical quantile mapping (EQM) and gamma-pareto quantile mapping (GPM), along with three non-stationary BC methods, detrended quantile mapping (DQM), quantile delta mapping (QDM), and robust quantile mapping (RQM), were selected to adjust daily biases between MME members and observations from the SJO weather station located in Costa Rica. The equidistant quantile-matching (EDQM) temporal disaggregation method was applied to obtain future sub-daily annual maximum precipitation series (AMPs) based on daily projections from the bias-corrected ensemble members. Both historical and future IDF curves were developed based on 5 min temporal resolution AMP series using the Generalized Extreme Value (GEV) distribution. The results indicate that projected future precipitation intensities (2020–2100) vary significantly from historical IDF curves (1970–2020), depending on individual GCM-RCMs, BC methods, durations, and return periods. Regardless of stationarity, the ensemble spread increases steadily with the return period, as uncertainties are further amplified with increasing return periods. Stationary BC methods show a wide variety of trends depending on individual GCM-RCM models, many of which are unrealistic and physically improbable. In contrast, non-stationary BC methods generally show a tendency towards higher precipitation intensities as the return period increases for individual GCM-RCMs, despite differences in the magnitude of changes. Precipitation intensities based on ensemble means are found to increase with the change factor (CF), ranging between 2 and 25% depending on the temporal scale, return period, and non-stationary BC method, with moderately smaller increases for short-durations and long-durations, and slightly higher for mid-durations. In summary, it can be concluded that stationary BC methods underperform compared to non-stationary BC methods. DQM and RQM are the most suitable BC methods for generating future IDF curves, recommending the use of ensemble means over ensemble medians or individual GCM-RCM outcomes.

Country-Scale Crop-Specific Phenology from Disaggregated PROBA-V

Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail for smaller plots. This study generated crop-specific phenomaps for mainland France (2016–2020) using PROBA-V data. A spatial disaggregation method reconstructed NDVI time series for individual crops within mixed pixels. Then, phenometrics were extracted from disaggregated PROBA-V and Sentinel-2 separately and compared to observed phenological stages. Results showed that PROBA-V-based phenomaps closely matched observations at regional level, with moderate accuracy at municipal level. PROBA-V demonstrated a higher detection rate than Sentinel-2, especially in cloudy periods, and successfully generated phenomaps before Sentinel-2B’s launch. The study highlights PROBA-V’s potential for operational crop monitoring, i.e., wheat heading and oilseed rape flowering, with performance comparable to Sentinel-2. PROBA-V outputs complement Sentinel-2: phenometrics cannot be generated at plot level but are efficiently produced at regional or national scales to study phenological gradients more easily than with Sentinel-2 and with similar accuracy. This approach could be extended to MODIS or SPOT-VGT, to generate historical phenological data, providing that a crop map is available.

A quantitative comparison of different methods for air conditioning energy consumption prediction in residential buildings

Accurate evaluation of energy consumption of air conditioning (AC) is crucial to energy efficiency in residential buildings, while few studies evaluate quantitatively the differences of the currently used methods. This study aimed to assess two common methods for AC energy consumption evaluation, namely, top-down total energy disaggregation and bottom-up energy simulation, through comparing to field measurement. A longitudinal investigation was conducted in residences in Chongqing, a typical city in the Hot Summer and Cold Winter(HSCW) zone. The yearly daily electricity consumption of 18 residences were collected and wireless sensors and smart sockets were used for onsite measurement. The AC energy consumption was obtained by splitting the total electricity consumption and measurements. Moreover, three typical reference buildings were built for predicting the annual AC energy consumption. Results showed that the simulated AC energy consumption was about 2.76 times of the measured value; while it was about 2.89 times for the top-down disaggregation method. Some key factors influencing AC energy consumption (e.g., indoor temperature distribution, opening duration, AC usage frequency) were identified for different methods. The work quantified the limitations of the top-down and bottom-up methods, contributing to the improvement of AC energy consumption evaluation methods and management of heating and cooling energy in residential buildings.

HiCPC: A new 10-km CMIP6 downscaled daily climate projections over China

Accurate climate projections are critical for various applications and impact assessments in environmental science and management. This study presents HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China), a novel dataset tailored to China’s specific needs. HiCPC leverages outputs from 22 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). To address inherent biases in daily GCM simulations, an advanced Bias Correction and Spatial Disaggregation (BCSD) method is employed, using the China Meteorological Forcing Dataset (CMFD) as a reference. HiCPC offers detailed daily precipitation and temperature data across China at an enhanced spatial resolution of 0.1° × 0.1°. It covers both the historical period (1979–2014) and future projections (2015–2100) based on four CMIP6 Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Upon validation, HiCPC demonstrates good performance, surpassing CMIP6 GCMs across the historical period. This reinforces its significance for essential research in climate change evaluation and its associated implications within China.

Disaggregation and Nowcasting of Regional GDP Series with a Simple Smoothing Algorithm

We propose a simple smoothing method for the spatiotemporal disaggregation of economic time series. Contrary to the existing methods, our approach does not require exogenous regressors and can therefore be used for countries lacking long and reliable series of regional economic indicators. The proposed method can also be applied sequentially, implying that one needs to revise only the estimates for the last low-frequency period when new data are disaggregated. This is a convenient feature when historical estimates are of interest. We apply this method to disaggregate annual real GDP data for Polish regions into quarterly series and compare the results with the series obtained from a multivariate linear regression-based procedure, considering the differences between the estimates and the consequences for regional recession dating. We also examine the nowcasting performance of the smoothing algorithm and find that it is superior to the regression-based alternative for most of the studied sample lengths and horizons up to a year.

Assessment of monthly to daily streamflow disaggregation methods: A case study of the Nile River Basin

Study regionThe Nile River Basin Study focusThe lack of observed streamflow data at a short time scale poses a critical challenge for calibrating and validating hydrologic models. Therefore, many disaggregation methods were developed, resulting in various relative performances without a clear indication of the optimal choice. This study aims to iteratively assess eight monthly to daily streamflow disaggregation methods at 21 major subbasin outlets in the Nile River Basin (NRB) to identify the best-performing ones. These methods include one proportionality method and seven interpolation methods, i.e., linear, 2nd-order spline, 3rd-order spline, Piecewise Cubic Hermite Interpolating Polynomial (Pchip), Modified Akima (MAkima), mean preserved 2nd-order spline, and mean preserved 3rd-order spline. We assessed these methods using three metrics and visual investigations. New hydrologic insights for the regionThe results showed that the interpolation methods performed well, better than the proportionality method. However, their performances decreased at stations with high daily streamflow fluctuations. The interpolation methods’ performances were similar in mimicking the daily values but significantly different in preserving the mass balance. The mean preserving 3rd-order interpolation method (Lai 22) was the best in preserving the mass balance and capturing the low, moderate and high flows and, therefore, selected to generate the daily flow data in the NRB. The results of this study can guide a reliable method for obtaining daily streamflow data, which is important for the hydrologic and water management studies in the NRB.

Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics.

Social and Environmental Determinants of Health (SEDH) provide us with a conceptual framework to gain insights into possible associations among different human behaviors and the corresponding health outcomes that take place often in and around complex built environments. Developing better built environments requires an understanding of those aspects of a community that are most likely to have a measurable impact on the target SEDH. Yet data on local characteristics at suitable spatial scales are often unavailable. We aim to address this issue by application of different data disaggregation methods. We applied different approaches to data disaggregation to obtain small area estimates of key behavioral risk factors, as well as geospatial measures of green space access and walkability for each zip code of Allegheny County in southwestern Pennsylvania. Tables and maps of local characteristics revealed their overall spatial distribution along with disparities therein across the county. While the top ranked zip codes by behavioral estimates generally have higher than the county's median individual income, this does not lead them to have higher than its median green space access or walkability. We demonstrated the utility of data disaggregation for addressing complex questions involving community-specific behavioral attributes and built environments with precision and rigor, which is especially useful for a diverse population. Thus, different types of data, when comparable at a common local scale, can provide key integrative insights for researchers and policymakers.

Optimal demand response for a virtual power plant with a hierarchical operation framework

There are significant uncertainties and variations in demand response under different operating conditions and incentives. These challenges hinder virtual power plants (VPPs) from fully leveraging the regulatory capabilities of flexible resources. To address this issue, this study introduces an optimal demand response (DR) approach for VPPs, featuring a hierarchical operational framework that accounts for the uncertainty in user participation in DR. First, a refined DR model is developed, incorporating response characteristic parameters based on the DR mechanism. Second, a two-stage distributionally robust optimization model is formulated within a market trading framework for energy scheduling of VPPs. This model aims to minimize operational costs while considering uncertainties in DR. Additionally, the ambiguity set for the uncertainty probability distributions is constructed using a data-driven method. Finally, the study presents an optimal DR disaggregation method that aims to maximize aggregator satisfaction and accounts for the impact of load rebound on the optimization results. Simulation results demonstrate that the proposed method significantly reduces the deviation in demand response by 40 % and enhances the profit of the VPP by 20.7 %.

Simulating appliance-level household electricity data: Accounting for residential behavior and usage patterns in China

Data scarcity presents a significant challenge for improving deep learning algorithms in Non-intrusive Load Disaggregation. These algorithms heavily rely on extensive appliance electricity consumption data, which is expensive to gather through on-site measurements. Therefore, generating simulated data has emerged as a viable alternative. However, existing approaches often randomly combine appliance power, failing to capture realistic household electricity usage patterns. To address this, this study investigates the dynamics of household electricity consumption throughout a 24-h day, considering factors such as household size, age, occupation, and education. Markov chains and Variational Auto-encoder are employed as a data generation model to simulate electrical power consumption data. Appliance power consumption data are aggregated to represent the total household load by incorporating resident electricity consumption behavior. Comparing the generated data with actual data from a self-measurement dataset, the Jensen-Shannon divergence, RMSE, and MAE are utilized to assess their similarity, and the Entropy and the Coefficient of Variation are utilized to evaluate randomness and volatility. The results indicate a high level of distribution similarity between the generated and actual data. Moreover, the observed variations show the model's capability to produce diverse and rich datasets. Overall, this research demonstrates the value of simulated data created as a low-cost method for Non-intrusive Load Disaggregation.

Reconstructing a Short-Term Indicator by State-Space Models: An Application to Estimate Hours Worked by Quarterly National Accounts

ISTAT has recently released an updated version of short-term statistics on hours worked in Italy, which are used in labor input estimates by the Quarterly National Accounts (QNA). The coverage of these statistics has been expanded from larger-than-ten workers firms to include the entire universe of Italian private firms. To include the updated indicator within estimates by QNA, the series must be reconstructed back to 1995 first quarter (1995q1) due to methodological requirements of QNA. In this paper, we first reconstruct the updated indicator using the Kalman filter and smoother algorithms applied to a state-space representation of a multivariate structural model (SUTSE). Next, we comparatively assess the performance of the new indicator against the non-updated one. This assessment is based on estimates of quarterly per-employee hours worked using temporal disaggregation methods for seven economic sections spanning the non-agricultural private business economy over the period 1995q1 to 2020q4. Compared to the previous indicator, the reconstructed indicator (i) implies improvements in temporal disaggregation model fitting in the majority of economic sections considered; (ii) returns smaller forecast errors in the 64.3% of the estimations, based on MAE; (iii) ensures a higher correlation between the estimated quarterly series to the indicator in the 71.4% of the estimates.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

Investigation of rainfall disaggregation with flexible timescales based on point process models

Analyzing high-temporal-resolution rainfall data with disaggregation tools is vital for understanding precipitation variability, especially in climate change studies with limited access to such data. To facilitate such study for tropical region, we proposed a Point-Process Modelling and Rainfall Disaggregation (PPMRD) framework for rainfall disaggregation with flexible time scales. The framework was tested on two tropical sites in Singapore, using six point-process models and four disaggregation schemes (24 h to 5 min, 3 h to 5 min, 24 h to 1 h, and 6 h to 1 h). Most models excelled fundamental statistics and extreme value analysis. The Bartlett-Lewis Rectangular Pulse Seven parameter (BLRP7) model was found to be the best performer for simulating and disaggregating tropical rainfall. The study findings emphasized the utility of disaggregation models in generating fine-resolution time series for analyzing rainfall extremes. However, it is crucial to underscore the importance of employing ensembles to comprehensively address uncertainties arising from the stochastic nature of rainfall. The study also emphasized the importance of model selection in the proposed framework, as the disaggregation method depends on the generator’s performance. The PPMRD surpasses traditional methods by handling subdaily upper-level series disaggregation to minute-level rainfall. This advancement offers greater capabilities and flexibility, promising benefits for generating high-temporal-resolution rainfall data in hydrological modeling and climate change impact studies.

Monitoring domestic water consumption: a comparative study of model-based and data-driven end-use disaggregation methods

ABSTRACT Monitoring the water usage of different appliances and informing consumers about it has been shown to have an impact on their behavior toward drinking water conservation. The most practical and cost-effective way to accomplish this is through a non-intrusive approach, that locally analyzes data received from a flow sensor at the main water supply pipe of a household. In this work, we present two different methods addressing the challenges of disaggregating end-use consumption and classifying consumption events. The first method is model-based (MB) and uses a combination of dynamic time wrapping and statistical bounds to analyze four water end-use characteristics. The second, learning-based (LB) method is data-driven and formulates the problem as a time-series classification problem without relying on a priori identification of events. We perform an extensive computational study that includes a comparison between an MB and an LB method, as well as an experimental study to demonstrate the application of the LB method on an edge computing device. Both methods achieve similar F1 scores (LB: 71.73%, MB: 71.04%) with the LB being more precise. The embedded LB method achieves a slightly higher score (72.01%) while enhancing on-site real-time processing, improving security and privacy and enabling cost savings.

“Impact of ESG disclosure on firm performance and cost of debt: Empirical evidence from India”

Despite several research evaluating the connection between sustainability and the bottom line of firms, the existing research is inconclusive. In addition, most studies focus on multinational firms based in the United States or Europe, while research in the context of emerging economies is scant. This paper endeavours to investigate the strength of the association between Environment, Social and Governance (ESG) disclosure scores and the financial performance of 272 Indian firms listed on National Stock Exchange for the period 2015 to 2021 by employing panel data regression techniques. The results indicate that organisations having better ESG performance outperform their industry peers financially and are valued more by the market. Moreover, the data indicates that financial markets for lenders consider ESG disclosure when determining creditworthiness. In accordance with this, businesses can examine their operations and become more aligned with eco-friendly methods. Even though ESG disclosure appears to be associated with improved financial success, this is not the case across all industries and during COVID-19 pandemic. This study is relevant due to the growing significance of sustainability and the role of emerging nations in international commerce. It helps managers prioritize resource allocations to ESG-related activities that may affect financial performance differently across industry sectors, thus adding to the literature on the financial effects of ESG disclosures, particularly industry-specific aspects. This research represents a pioneering effort by assessing the impact of ESG disclosure on the profitability, market value, and cost of financing of Indian firms using both aggregation and disaggregation methods.

Disaggregation Model of Tigris River Inflow into a Proposed Makhol Reservoir Using Parametric Approach

Since Makhol dam is planned to be constructed on Tigris River to the north of Baiji discharge measurement station, it is essential to study the nature of inflow into this reservoir. The information concerning this inflow is of great help in operating and management of the prospective reservoir. From our point of view, it is necessary to know how these inflows are distributed and contributed to Tigris from different upstream sources. Disaggregation flow models are stochastic generation techniques, that used to divided data into lower time scales from higher time scales using parametric approaches with two main categories: spatial and temporal. In the streamflow disaggregation model, historical data statistics (mean, skewness, standard deviation, maximum, and minimum) can be preserved while distributing single-site values to several sites in space and time. In this study, the aggregated streamflows data at a key station will be disaggregated into a corresponding series of discharges at sub-stations that are statistically similar to those observed by applying Stochastic Analysis Modeling and Simulation (SAMS 2010) software. To investigate the appropriate the disaggregation method for modeling monthly flow data, we used the annual and monthly data flow of five gauging stations in the Tigres River in Iraq (Mosul Dam station on Tigris river, Asmawah on AlKhazir river, Eski Kalak on Upper Zab, Dibs Dam on Lower Zab, and Baiji station on Tigris river) for the duration 2000–2020. The application approach's statistical outcomes were contrasted with their historical counterparts and the results showed that most years and months at all stations were in good agreement with the historical data. Therefore, we argue that this method have ability to be used when making decisions about water management strategies in these regions which is essential for water resource managers and decision makers.

Equation of heavy rainfall using the rainfall disaggregation method for the Farinha river basin – MA

Given that rain is one of the main active agents in the process of water protection, it is extremely important to evaluate the response of the soil to different precipitation, both in terms of the volume precipitated and the duration and characteristic of the event. To this end, one of the most recommended methods for the transmission of heavy rainfall is the rainfall disaggregation method (DAEE-CETESB, 1980), which consists of obtaining transfers that relate the maximum 24-hour rainfall in pluviographs and 1-day rainfall in pluviometers and, subsequently, rainfall with shorter durations. In view of the above, this work aims to determine, by means of the rainfall disaggregation method, the projection of heavy rainfall for the Farinha river basin - MA and, by means of this projection, to quantify which rainfall intensities can generate erosive effects to the soil. The parameters of the IDF prescription were adjusted: K = 252.77; a = 843.42; b = 10, and; c = 0.742, which allowed generating a slope coefficient (R²), of 1.0, reflecting the quality of the adjustment.This projection of the protection of intense rainfall for the Farinha river basin - MA, allows a series of regionalized hydrological studies, such as: estimates of rainfall erosivity, studies of water and soil losses by simulated rains, quantification of the intensity of erosive processes resulting from water infiltration, drainage sizing, spillways, protection works, floods and floods among others.

Using Geographic Disaggregation to Compare Tuberculosis Epidemiology Among American Indian and Alaska Native Persons-USA, 2010-2020.

American Indian and Alaska Native (AIAN) populations are frequently associated with the highest rates of tuberculosis (TB) disease of any racial/ethnic group in the USA. We systematically investigated variation in patterns and potential drivers of TB epidemiology among geographically distinct AIAN subgroups. Using data reported to the National Tuberculosis Surveillance System during 2010-2020, we applied a geographic method of data disaggregation to compare annual TB incidence and the frequency of TB patient characteristics among AIAN persons in Alaska with AIAN persons in other states. We used US Census data to compare the prevalence of substandard housing conditions in AIAN communities in these two geographic areas. The average annual age-adjusted TB incidence among AIAN persons in Alaska was 21 times higher than among AIAN persons in other states. Compared to AIAN TB patients in other states, AIAN TB patients in Alaska were associated with significantly higher frequencies of multiple epidemiologic TB risk factors (e.g., attribution of TB disease to recent transmission, previous diagnosis of TB disease) and significantly lower frequencies of multiple clinical risk factors for TB disease (e.g., diagnosis with diabetes mellitus, end-stage renal disease). Occupied housing units in AIAN communities in Alaska were associated with significantly higher frequencies of multiple measures of substandard housing conditions compared to AIAN communities in other states. Observed differences in patient characteristics and substandard housing conditions are consistent with contrasting syndromes of TB epidemiology in geographically distinct AIAN subgroups and suggest ways that associated public health interventions could be tailored to improve efficacy.