Peak Electricity Demand and Global Warming in the Industrial and Residential areas of Pune : An Extreme Value Approach

Ayush Maheshwari; Kamal Kumar Murari; T. Jayaraman

REVIEW 4 major objections 7 minor 5 references

Peak Electricity Demand and Global Warming in the Industrial and Residential areas of Pune : An Extreme Value Approach

T0 review · 4 major / 7 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Using five years of feeder-level electricity data from Pune, this paper claims that residential electricity demand is temperature-sensitive—about 1.5-2% per 1°C of apparent temperature—while industrial demand is not, and that…

desk verdict The sectoral Pune load–temperature dataset is new and the regression results are believable, but a degrees-of-freedom error in the GEV likelihood-ratio test invalidates the paper's headline claim about residential peak demand. read the letter →

arxiv 1908.08570 v1 pith:ONNVNI5O submitted 2019-08-22 stat.AP

Ayush Maheshwari , Kamal Kumar Murari , T. Jayaraman This is my paper · ORCID

classification stat.AP MSC 62G3262P1262F03

keywords electricitydemandpeakloadapparenttemperatureextremevaluetheoryGEVPuneglobalwarmingsectoralanalysis

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether rising temperatures from global warming push up peak electricity demand in a subtropical Indian city, and whether the effect differs between industrial and residential consumers. Using five years of feeder-level electricity data from Pune, the authors separate divisions by their mix of sheddable and non-sheddable feeders and compare electricity demand with apparent temperature. They find industrial electricity use is largely insensitive to temperature, while residential demand rises by roughly 1.5-2% per 1°C of apparent temperature on average. Applying stationary and non-stationary generalized extreme value models to block maxima of standardized daily demand, they find apparent temperature significantly explains peak demand in residential and mixed divisions but not in the industrial division. If correct, climate-driven warming would translate into rising peak loads mainly from homes, a signal for electricity infrastructure planning.

What carries the argument

The central mechanism is the generalized extreme value (GEV) distribution applied to block maxima: the distribution for the largest standardized demand in each week, fortnight, or month. The non-stationary version lets the location and log-scale be linear functions of apparent temperature, $\mu(t) = \mu_0 + \mu_1 t$ and $\log\sigma(t) = \sigma_0 + \sigma_1 t$, and compares nested models using the likelihood-ratio statistic $2(\mathrm{nllh}(M_1)-\mathrm{nllh}(M_2))$ against a $\chi^2_1$ reference with a 95% threshold of 3.84. Apparent temperature itself is built from daily average temperature, humidity, wind speed, and water vapor pressure using the Steadman formula, so the covariate carries both heat and moisture. This machinery decides whether temperature explains the peaks, not just the average, of electricity demand.

What would settle it

Take the same five years of Pune feeder data, extract the true hourly or sub-daily peak demand for each block, and fit the same stationary and non-stationary GEV models with apparent temperature as covariate. If the likelihood-ratio statistics and temperature slopes for residential divisions weaken or vanish, the paper's peak-load claim would fail.

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Extended reading notes

Core claim

The central claim is that in Pune over 2008-2012, industrial electricity demand is essentially decoupled from temperature while residential demand is temperature-sensitive, and that the temperature signal reaches the upper tail of demand. The evidence is a year-fixed-effects regression giving slopes of 15.9 MW/°C for the industrial Bhosari division versus 40.8, 43.4, and 45.7 MW/°C for residential and mixed divisions, summarized in the abstract as a 1.5-2% change in average residential demand per degree. In the extreme-value analysis, allowing the GEV location and log-scale to depend linearly on apparent temperature improves the fit for Kothrud at the 90% significance level and for Pimpri and Shivaji Nagar at the 95% level, but not for Bhosari. The authors conclude that non-stationary GEV models with apparent temperature as a covariate can capture climate-driven peak electricity load in residential areas.

Load-bearing premise

The load-bearing assumption is that the block-by-block maximum of each day's total electricity use stands in for peak electricity demand. If the true peak that stresses the grid is the highest hourly or sub-daily load, the extreme-value model targets a different quantity.

Editorial extensions

If this is right

Residential peak load in Pune can be expected to rise as global warming increases apparent temperature, while industrial peak demand should remain nearly flat.
Utilities can use fitted non-stationary GEV models with temperature as a covariate to estimate return levels of peak demand under warmer future climates.
City-level aggregation would dilute or hide the residential temperature sensitivity, so sectorally disaggregated feeder data is needed for accurate climate-impact assessment.
The same workflow can be transferred to other subtropical and humid cities with feeder-level electricity data and station-level weather data.
Accounting for climate-driven peak load would support planning for distribution capacity, demand response, and cooling-related infrastructure investment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The reported 1.5-2% average residential demand sensitivity probably understates the peak sensitivity, because cooling load concentrates in hot afternoon hours; fitting the same GEV models to hourly or sub-daily maxima would test this directly.
As income and air-conditioning penetration rise, the residential temperature slope may steepen, making the 2008-2012 estimates a plausible lower bound for future warming impacts in Pune.
If the true system peak is sub-daily, analyzing daily total demand could misdescribe peak behavior; comparing daily-sum block maxima with true hourly peaks would show whether the method needs adjustment.
Applying the same apparent-temperature GEV approach to other tropical and subtropical cities could reveal whether a common residential load-temperature response curve emerges across humid climates.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague, before you cite this paper, know two things. The sectoral dataset is genuinely new, but the GEV likelihood-ratio test that supports the headline abstract claim has an internal inconsistency that flips the conclusion.

The LRT as run compares a stationary GEV to a non-stationary GEV with both location and log-scale varying with temperature. That's two added parameters, so the 95% critical value is 5.99, not the 3.84 used in Section 3.2.4. With the correct df=2, only Pimpri (LRT=7.30) is significant at 95%. Shivaji Nagar (5.01) and Kothrud (3.61) are not. The paper's own text claims Shivaji Nagar significant at 95% and Kothrud at 90%; under df=2, neither holds. Since those are the residential divisions the abstract points to, the claim that residential peak demand is significantly temperature-driven is not supported by the reported test. This is a load-bearing flaw, though the regression analysis still stands.

What's new and useful: feeder-level disaggregation for an Indian subtropical city, with a plausible division classification from sheddable/non-sheddable feeders. The year-fixed-effects regression shows residential slopes (40.8, 43.4 MW/°C) about 2.5x the industrial Bhosari slope (15.9), and the in-sample fit is good. That sectoral contrast is a real empirical contribution for utility planners.

Soft spots aside from the LRT: the 'peak' is actually block maxima of standardized daily total demand, not hourly peak load, so the title overreaches. The 1.5-2% per degree figure does not appear anywhere in the tables—only MW slopes are given. The global-warming framing is an extrapolation without scenario runs or return-level estimates. No code or data are archived.

Overall: this is useful applied science with a questionable central statistical test. The paper deserves peer review because the dataset is unique and the sectoral question matters, but a serious referee should demand a corrected LRT, a peak-load definition matching the claim, and a traceable percentage sensitivity.

For you: read if you work on India load forecasting or climate-demand; otherwise it's a cautionary example of an otherwise decent empirical study undermined by a degrees-of-freedom mistake.

Referee Report

4 major / 7 minor

Summary. The paper analyzes feeder-level electricity demand data for four divisions of Pune (Bhosari, Kothrud, Pimpri, Shivaji Nagar) over 2008–2012, classifies divisions as industrial, residential, or mixed, and relates daily electricity demand to apparent temperature. It uses year-fixed-effects regressions and stationary and non-stationary GEV models with apparent temperature as a covariate. The authors conclude that industrial demand is largely temperature-insensitive, that residential demand shows roughly 1.5–2% change per degree of apparent temperature, and that non-stationary GEV models show peak electricity demand in residential areas is significantly influenced by temperature.

Significance. The disaggregated feeder-level dataset for an Indian city is a useful and relatively rare contribution, and the comparison of industrial versus residential temperature sensitivity is policy-relevant for electricity distribution planning. The fixed-effects regression is a sensible way to control for changing feeder composition, and the use of GEV models with temperature covariates is an appropriate framework for extreme-demand analysis. However, the headline claim about "peak electricity demand" is not directly supported by the analysis as written, and the likelihood-ratio test has an inconsistency in its degrees of freedom. If these issues are corrected, the paper could provide a credible estimate of temperature sensitivity of daily electricity demand extremes in Pune.

major comments (4)

[Section 3.2.3] The block-maxima variable defined in Section 3.2.3 is a standardized daily total electricity demand per block, not an hourly or sub-daily peak load. The equation states that the block maximum is computed from ED_i, where ED_i is "the daily sum of electricity demand for block i," standardized by block mean and standard deviation. The title and abstract claim "peak electricity demand," but the GEV analysis estimates extremes of daily total demand. Please either obtain and use sub-daily peak-load data or revise the title, abstract, and conclusions to refer to extremes of daily total demand rather than peak load.
[Section 3.2.4 and Table 2 (GEV table)] The likelihood-ratio test is described in Section 3.2.4 as comparing Model 1 (only μ varying) with Model 2 (μ and σ varying), using a chi-squared distribution with one degree of freedom and a 95% critical value of 3.84. However, Table 2 defines the test statistic as 2*(nllh(non-stationary) - nllh(stationary)), which adds two parameters (μ1 and σ1) and should be compared with a chi-squared distribution with two degrees of freedom, whose 95% critical value is 5.99. Under the correct two-degree-of-freedom test, only Pimpri (7.30) is significant at 95%; Shivaji Nagar (5.01) and Kothrud (3.61) are not. This directly affects the abstract's claim that residential peak demand is significantly influenced by temperature. Please clarify which nested comparison is intended and correct the text and table accordingly.
[Abstract and Section 4 regression results] The abstract states that residential activities show "around 1.5-2% change in average electricity demand with 1 degree rise in AT," but no such percentage calculation appears in Section 4 or in Table 2, which reports only slopes in MW. The text after Table 2 says percentage changes will be small but does not provide the computed percentages or confidence intervals. Please add the percentage-change calculation with uncertainty bounds, or remove the quantitative claim from the abstract.
[Section 5 (Conclusion)] The conclusion states that the study "predicts that there will be a significant rise of peak electricity load as a consequence of the increase in temperatures," but the paper does not present future temperature scenarios, climate projections, or a prediction exercise. The analysis estimates historical temperature sensitivity only. Please temper the prediction language or add an explicit scenario-based projection.

minor comments (7)

[Section numbering] The section numbering is inconsistent: Section 3.2.1 is followed by Section 3.2.3, and Section 4.1 is followed by Section 4.3 with no Section 4.2. Please renumber the sections.
[Section 4.3] The paragraph beginning "The maximum electricity demand of 15 days non-overlapping block is fitted to the GEV distribution..." is repeated verbatim, and the second repetition contains an incomplete sentence. Please remove the duplication.
[Table numbering] Table 2 is used twice: once for the year-fixed-effects regression results and once for the GEV parameter estimates. Please renumber the tables and update all cross-references.
[Figure 5 caption] Figure 5 is captioned as "Q–Q plots obtained by plotting values from the GEV stationary model against the empirical values," but the surrounding text in Section 4.3 refers to diagnostic plots for the non-stationary model. Please clarify which model the figure displays.
[Section 3.2.1] The Steadman equation for apparent temperature is garbled in the text: "T T0.33 0.7 A = a + * e − * w − 4" is not readable as a formula. Please typeset the equation properly and define all variables.
[Section 3.2.3] The statement that the non-stationary model "would assume that present day temperature is dependent on the previous day's temperature" is inaccurate; the non-stationary GEV model here uses apparent temperature as a covariate and does not impose an autoregressive structure. Please correct this description.
[References] Some references have inconsistent formatting and missing bibliographic details (e.g., Murari et al. 2015, Revadekar et al. 2012 are cited in the text but the reference list entries are incomplete or misnumbered). Please standardize the reference list.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper's claims are empirical fits and standard model comparisons, not derivations that reduce to their inputs.

full rationale

The paper's derivation chain is an empirical regression and GEV model comparison, not a derivation that returns its own inputs. The fixed-effects regression of daily electricity demand on apparent temperature estimates slopes (Table 2), and the abstract's 1.5-2% per-degree claim is a transformation of those fitted slopes relative to demand levels; this is a fitted estimate, not a prediction that is identical to its fitting input by construction. The non-stationary GEV analysis fits block maxima with mu(t) and log sigma(t) linear in apparent temperature and compares negative log-likelihood values via a likelihood-ratio test; this is a standard model comparison, and the temperature coefficients are not defined in terms of the test statistic. No uniqueness theorem or ansatz is imported from the authors' prior work. The only co-authored citation (Murari et al., cited in the introduction for increased extreme temperature days) is background context and is not load-bearing for the electricity-demand result. The block-maxima standardization defines 'peak' as maxima of standardized daily total demand, which is a potential validity limitation for the stated 'peak' claim but is not circularity. The likelihood-ratio test's degrees of freedom are stated inconsistently (Section 3.2.4 uses one degree of freedom while the Table 2 caption implies a two-parameter comparison); this affects which divisions are significant and is a statistical correctness issue, not a circularity issue. Overall, the central claims are self-contained empirical findings with no circular reduction, so the circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard EVT asymptotics plus several domain assumptions about feeder classification, omitted variables, day exclusions, and the use of daily total rather than hourly peak demand. The estimated coefficients are fitted to one city's five-year record, so the generalizability claim is an extrapolation beyond the fitted period. No new physical entities, forces, dimensions, or conserved quantities are introduced.

free parameters (5)

Regression temperature slope (MW per °C apparent temperature) = Bhosari 15.9; Kothrud 40.8; Pimpri 45.7; Shivaji Nagar 43.4
Fitted by year-fixed-effects regression on Pune feeder data, 2008-2012; this slope underlies the 1.5-2% residential sensitivity claim.
GEV location trend μ1 = Bhosari 0.0093; Kothrud 0.0156; Pimpri 0.0220; Shivaji Nagar 0.0219
Maximum-likelihood estimate from the non-stationary GEV fit; it is the temperature coefficient in the location equation.
GEV log-scale trend σ1 = Bhosari -0.0009; Kothrud 0.0358; Pimpri 0.0089; Shivaji Nagar -0.0045
Maximum-likelihood estimate from the non-stationary GEV fit; the likelihood-ratio test compares models with and without this term.
GEV shape parameter ξ = Bhosari -0.0981; Kothrud -0.1075; Pimpri -0.0840; Shivaji Nagar -0.0152
Fitted shape parameter in the non-stationary GEV; affects tail behavior but is not the main temperature-sensitivity estimate.
Block size for GEV block maxima = 7, 15 and 30 days; reported results use 15-day blocks
Chosen by hand rather than estimated; the paper tests three sizes to reduce arbitrariness, but the headline likelihood-ratio tests use 15-day blocks.

assumptions (5)

standard math Block maxima of the standardized daily demand series follow a GEV distribution.
Invoked in Section 3.2.3; extreme value theory justifies GEV asymptotically, but the exact block size of 15 days is a finite-sample approximation.
domain assumption Feeder sheddable and non-sheddable status, aggregated to divisions, identifies residential versus industrial activity.
Section 3.1.2 and Table 1. The sectoral comparison collapses if feeder classification, or reclassification during 2008-2012, misleads.
domain assumption Year fixed effects hold other demand drivers, such as income, price, population, and appliance ownership, constant within years.
Section 3.2.3 and Table 2; no direct controls for these variables are included, so the temperature coefficient may absorb correlated within-year variation.
domain assumption The Steadman apparent temperature formula with average daily inputs is an appropriate thermal stress measure for Pune.
Section 3.2.1; the formula is taken from prior literature, and the result depends on using apparent temperature rather than dry-bulb temperature alone.
domain assumption Excluding Thursdays for industrial divisions and Sundays for residential divisions removes non-working-day bias without altering the temperature signal.
Section 3.2.3; these are data exclusions that change the block-maxima series and could affect which days contribute to the extreme-value fit.

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Cite this review

Pith. "Pith review of Peak Electricity Demand and Global Warming in the Industrial and Residential areas of Pune : An Extreme Value Approach." pith.science (2026). https://pith.science/paper/ONNVNI5O

@misc{pith2026190808570,
  author       = {Pith},
  title        = {Pith review of: Peak Electricity Demand and Global Warming in the Industrial and Residential areas of Pune : An Extreme Value Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ONNVNI5O}},
  note         = {Machine review of arXiv:1908.08570}
}

read the original abstract

Industrial and residential activities respond distinctly to electricity demand on temperature. Due to increasing temperature trend on account of global warming, its impact on peak electricity demand is a proxy for effective management of electricity infrastructure. Few studies explore the relationship between electricity demand and temperature changes in industrial areas in India mainly due to the limitation of data. The precise role of industrial and residential activities response to the temperature is not explored in sub-tropical humid climate of India. Here, we show the temperature sensitivity of industrial and residential areas in the city of Pune, Maharashtra by keeping other influencing variables on electricity demand as constant. The study seeks to estimate the behaviour of peak electricity demand with the apparent temperature (AT) using the Extreme Value Theory. Our analysis shows that industrial activities are not much influenced by the temperature whereas residential activities show around 1.5-2% change in average electricity demand with 1 degree rise in AT. Further, we show that peak electricity demand in residential areas, performed using stationary and non-stationary GEV models, are significantly influenced by the rise in temperature. The study shows that with the improvement in data collection, better planning for the future development, accounting for the climate change effects, will enhance the effectiveness of electricity distribution system. The study is limited to the geographical area of Pune. However, the methods are useful in estimating the peak power load attributed to climate change to other geographical regions located in subtropical and humid climate.

Figures

Figures reproduced from arXiv: 1908.08570 by the authors.

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Works this paper leans on

5 extracted references · 5 canonical work pages

[2]

Application of a hierarchical model for city competitiveness in cities of India

Description of study area In this study, we analyse extreme temperature and peak electricity demand relationship. Pune, situated close to Western coast, is the 7 th most populated city in India covering an area of 458 km 2 . The urban agglomeration consists of the population of about 3.3 million, is considered to be fastest growing urban center in I...

work page 2013
[3]

The data on climatic factors is obtained from www.TuTiempo.net which gives station-wise data for all major weather stations in India

Data and Methods 3.1 Data 3.1.1 Climatic data The daily temperature data and relative humidity was obtained for the Pune meteorological station from the period from January 2008 - December 2012. The data on climatic factors is obtained from www.TuTiempo.net which gives station-wise data for all major weather stations in India. The data is collected for ...

work page 1994
[4]

Figure 1 shows the monthly pattern of electricity demand from January to December and its variation with temperature

Results and Discussion The study shows the results of the analysis performed between the period 2008-2012 for the four divisions. Figure 1 shows the monthly pattern of electricity demand from January to December and its variation with temperature. In the Bhosari division, it is apparent that the monthly distribution of ED is not responsive to the changes ...

work page 2008
[5]

For the developing country like India located in sub-tropical and humid climate, this study quantified the change in electricity demand attributed to the change in temperatures

Conclusion In this paper, we addressed the problem of the impact of the change in electricity demand due to rising temperatures. For the developing country like India located in sub-tropical and humid climate, this study quantified the change in electricity demand attributed to the change in temperatures. The key contribution of this paper is to present t...

work page
[6]

A decade of weather extremes

References 1 . Coumou, Dim, and Stefan Rahmstorf. "A decade of weather extremes." Nature Climate Change 2.7 (2012): 491-496. 2. Mishra, V., A. R. Ganguly, B. Nijssen, and D. P. Lettenmaier (2015). Changes in observed climate extremes in global urban areas. Environmental Research Letters 10(2), 024005. 3. McMichael, Anthony J. "Insights from past millennia...

work page 2012

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [2]

Application of a hierarchical model for city competitiveness in cities of India

Description of study area In this study, we analyse extreme temperature and peak electricity demand relationship. Pune, situated close to Western coast, is the 7 th most populated city in India covering an area of 458 km 2 . The urban agglomeration consists of the population of about 3.3 million, is considered to be fastest growing urban center in I...

work page 2013

[2] [3]

The data on climatic factors is obtained from www.TuTiempo.net which gives station-wise data for all major weather stations in India

Data and Methods 3.1 Data 3.1.1 Climatic data The daily temperature data and relative humidity was obtained for the Pune meteorological station from the period from January 2008 - December 2012. The data on climatic factors is obtained from www.TuTiempo.net which gives station-wise data for all major weather stations in India. The data is collected for ...

work page 1994

[3] [4]

Figure 1 shows the monthly pattern of electricity demand from January to December and its variation with temperature

Results and Discussion The study shows the results of the analysis performed between the period 2008-2012 for the four divisions. Figure 1 shows the monthly pattern of electricity demand from January to December and its variation with temperature. In the Bhosari division, it is apparent that the monthly distribution of ED is not responsive to the changes ...

work page 2008

[4] [5]

For the developing country like India located in sub-tropical and humid climate, this study quantified the change in electricity demand attributed to the change in temperatures

Conclusion In this paper, we addressed the problem of the impact of the change in electricity demand due to rising temperatures. For the developing country like India located in sub-tropical and humid climate, this study quantified the change in electricity demand attributed to the change in temperatures. The key contribution of this paper is to present t...

work page

[5] [6]

A decade of weather extremes

References 1 . Coumou, Dim, and Stefan Rahmstorf. "A decade of weather extremes." Nature Climate Change 2.7 (2012): 491-496. 2. Mishra, V., A. R. Ganguly, B. Nijssen, and D. P. Lettenmaier (2015). Changes in observed climate extremes in global urban areas. Environmental Research Letters 10(2), 024005. 3. McMichael, Anthony J. "Insights from past millennia...

work page 2012