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Targeting Customers for Demand Response Based on Big Data

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Jungsuk Kwac, Ram Rajagopal

Applications · Submitted 14 Sep 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We propose a scalable methodology for demand response targeting utilizing novel data available from smart meters.

how it is stated · asserted 1.00 read the record →

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arXiv ID1409.4119 (version 1)
Pith IDE75OD2V7, Pith's permanent record of this paper and its review
CitationJungsuk Kwac, Ram Rajagopal. "Targeting Customers for Demand Response Based on Big Data." arXiv:1409.4119 (2014). https://pith.science/paper/E75OD2V7
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,688 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.09 · states limitation 0.10 · human participants 0.21 · controlled comparison 0.09 · randomized assignment 0.02 · states quantitative result 0.13 · compares with prior results 0.22 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Selecting customers for demand response programs is challenging and existing methodologies are hard to scale and poor in performance. The existing methods were limited by lack of temporal consumption information at the individual customer level. We propose a scalable methodology for demand response targeting utilizing novel data available from smart meters. The approach relies on formulating the problem as a stochastic integer program involving predicted customer responses. A novel approximation is developed algorithm so it can scale to problems involving millions of customers. The methodology is tested experimentally using real utility data.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Selecting customers for demand response programs is challenging and existing methodologies are hard to scale and poor in performance. background 0.93 0.07 0.04 asserted 1.00
s1 The existing methods were limited by lack of temporal consumption information at the individual customer level. gap 0.86 0.04 0.02 asserted 1.00
s2 We propose a scalable methodology for demand response targeting utilizing novel data available from smart meters. contribution 0.99 0.90 0.19 asserted 1.00
s3 The approach relies on formulating the problem as a stochastic integer program involving predicted customer responses. method 0.97 0.86 0.07 asserted 1.00
s4 A novel approximation is developed algorithm so it can scale to problems involving millions of customers. method 0.70 0.94 0.12 asserted 1.00
s5 The methodology is tested experimentally using real utility data. method 0.81 0.94 0.09 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.49.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Selecting customers for demand response programs is challenging and existing methodologies are hard to scale and poor in performance. The existing methods were limited by lack of temporal consumption information at the individual customer level. We propose a scalable methodology for demand response targeting utilizing novel data available from smart meters. The approach relies on formulating the problem as a stochastic integer program involving predicted customer responses. A novel approximation is developed algorithm so it can scale to problems involving millions of customers. The methodology is tested experimentally using real utility data.

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