Orthogonalization and machine learning methods for residential energy estimation with social and economic indicators

Lawal, Abiola S.; Servadio, Joseph L.; Davis, Tate; Ramaswami, Anu; Botchwey, Nisha; Russell, Armistead G.

doi:10.1016/j.apenergy.2020.116114

Cited by 7 publications

(3 citation statements)

References 76 publications

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“…This case explored residential energy use patterns for natural gas and electricity considering multiple SEIU determinants in metropolitan Atlanta (Lawal et al., 2021). Energy use data for electricity and natural gas (infrastructure [ I ] consumption attributes) were obtained from utilities at the zip‐code level and matched with social ( S ) (e.g., income, education), urban form ( U ) (e.g., impervious surface), and environmental sustainability ( E ) data (i.e., air pollution concentrations from a fine‐grained model).…”

Section: Case Studies Measuring Intra‐urban Inequality and Equity Usi...mentioning

confidence: 99%

A data framework for assessing social inequality and equity in multi‐sector social, ecological, infrastructural urban systems: Focus on fine‐spatial scales

Clark

Tabory

Tong

et al. 2022

J of Industrial Ecology

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Cities are increasingly advancing multiple societal goals related to environmental sustainability, health, well‐being, and equity. However, there are few comprehensive data sets that address social inequality and equity across multiple infrastructure sectors, determinants, and outcomes, particularly at fine intra‐urban spatial scales. This paper: (1) Offers an overarching conceptualization of inequality and equity in multi‐sector urban systems; (2) Introduces a broad data framework to assess inequality and equity across social (S), ecological (E), infrastructural (I), and urban (U) form determinants (SEIU) and environment (E), health (H), well‐being (W), and economy and security (E) outcomes (EHWE), identifying a universe of >110 SEIU–EHWE data layers (variables) of interest; (3) Provides an illustrative data case study of a US city that synthesizes publicly available sources of the associated SEIU–EHWE data attributes, noting their availability/gaps at fine spatial scales, important to inform social inequality; (4) Discusses analytic methods to quantify inequality and spatial correlates across SEIU determinants and EHWE outcomes; and, (5) Demonstrates several use‐cases of the data framework and companion analytic methods through real‐world applied case studies that inform equity planning in applications ranging from energy sector investments to air pollution and health. The US data case study reveals data availability (covering 41 of the 113 data layers) as well as major gaps associated with EHWE outcomes at fine spatial scales, while the application examples demonstrate practical use. Overall, the SEIU–EHWE data framework provides an anchor for systematically gathering, analyzing, and informing multiple dimensions of inequality and equity in sustainable urban systems.

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Section: Case Studies Measuring Intra‐urban Inequality and Equity Usi...mentioning

confidence: 99%

A data framework for assessing social inequality and equity in multi‐sector social, ecological, infrastructural urban systems: Focus on fine‐spatial scales

Clark

Tabory

Tong

et al. 2022

J of Industrial Ecology

Self Cite

View full text Add to dashboard Cite

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“…To assess the forecasting performance of a model, three alternative forecasting accuracy measures were employed in this study, namely the mean absolute percentage error (MAPE) [19], the root mean square error (RMSE) [36], and the normalized root mean square error (NRMSE) [37]. Definitions of these measures are presented in the following expressions ( 7)- (9).…”

Section: Performance Measurementioning

confidence: 99%

Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting

Deng¹,

Zhang²,

Huang³

et al. 2021

Energies

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Electricity consumption forecasting plays an important role in investment planning of electricity infrastructure, and in electricity production/generation and distribution. Accurate electricity consumption prediction over the mid/long term is of great interest to both practitioners and academics. Considering that monthly electricity consumption series usually show an obvious seasonal variation due to their inherent nature subject to temperature during the year, in this paper, seasonal exponential smoothing (SES) models were employed as the modeling technique, and the particle swarm optimization (PSO) algorithm was applied to find a set of near-optimal smoothing parameters. Quantitative and comprehensive assessments were performed with two real-world electricity consumption datasets on the basis of prediction accuracy and computational cost. The experimental results indicated that (1) whether the accuracy measure or the elapsed time was considered, the PSO performed better than grid search (GS) or genetic algorithm (GA); (2) the proposed PSO-based SES model with a non-trend component and additive seasonality term significantly outperformed other competitors for the majority of prediction horizons, which indicates that the model could be a promising alternative for electricity consumption forecasting.

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“…Several studies have used machine learning algorithms to identify and predict energy consumption in residents. For example, a research team at Princeton University achieved increased accuracy and speed in detecting energy consumption in households using deep learning algorithms [17]. Similarly, researchers at Stanford University improved the detection and prediction accuracy of energy consumption in households using stationary wavelet transform [18].…”

Section: Introductionmentioning

confidence: 99%

Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption

Huang¹,

Wang

2023

Applied Sciences

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As the fertility rate declines, it becomes increasingly necessary for governments to guide power companies in introducing preferential tariffs to encourage nuclear families to have children. However, traditional household statistics for residential households are time-consuming and insufficient for enterprises seeking to adopt intelligent marketing schemes for different types of households. To address these issues, this paper proposes a nuclear family type identification method for residential electricity consumption based on a deep forest algorithm. The method first classifies nuclear households according to the number of children in them. Then, features are selected by combining the daily 48-point load and prior knowledge of nuclear families. The Pearson correlation coefficient and random forest importance ranking are used to remove features with low correlation and low importance. Additionally, features are classified based on their importance, and the number of features is balanced by stratified sampling to optimize the multi-granularity scan results and improve the model’s generalization. Finally, the improved cascade forest with feature input replacement base learner is trained, and the model is evaluated using accuracy evaluation metrics.The experimental results demonstrate that the proposed model accurately recognizes the number of children in different nuclear families and can be used in power companies to improve lean management. The results show that the improved method is effective in improving recognition com-pared to the original deep forest method, with recognition accuracy 5.1% higher than the random forest method and 0.7% higher than the deep forest method, reaching 94%.

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Orthogonalization and machine learning methods for residential energy estimation with social and economic indicators

Cited by 7 publications

References 76 publications

A data framework for assessing social inequality and equity in multi‐sector social, ecological, infrastructural urban systems: Focus on fine‐spatial scales

A data framework for assessing social inequality and equity in multi‐sector social, ecological, infrastructural urban systems: Focus on fine‐spatial scales

Equipping Seasonal Exponential Smoothing Models with Particle Swarm Optimization Algorithm for Electricity Consumption Forecasting

Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption

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