Leveraging Surplus Electricity: Profitability of Bitcoin Mining as a National Strategy in South Korea

Jaehong Jeong; Jungsoon Choi; Yoonseul Choi

REVIEW 4 major objections 4 minor 24 references

Leveraging Surplus Electricity: Profitability of Bitcoin Mining as a National Strategy in South Korea

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A new study argues that South Korea's leftover solar electricity could mine $294–348 million in Bitcoin profit per year.

desk verdict A transparent but physically unrealistic profitability estimate for Bitcoin mining on South Korean solar surplus; the 24-hour availability assumption alone likely flips the headline numbers. read the letter →

arxiv 2505.00303 v1 pith:JEJPOWPK submitted 2025-05-01 stat.AP stat.ML

Yoonseul Choi , Jaehong Jeong , Jungsoon Choi This is my paper · ORCID

classification stat.APstat.ML

keywords surpluselectricityBitcoinminingnetmeteringKEPCORandomForestLSTMsolarenergyprofitabilityanalysis

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 tries to establish that surplus electricity from household solar systems, currently left unused after net metering, can be profitably redirected to Bitcoin mining. The authors calculate that operating 30,565 to 45,439 Antminer S21 XP Hyd units on that surplus would generate roughly $294 to $348 million in profit over 12 months after equipment depreciation. They argue this revenue stream could help Korea Electric Power Corporation (KEPCO) reduce its debt, minimize wasted energy, and resolve unsettled payment issues from net-metered solar households. The study is an applied empirical analysis: it uses actual surplus electricity data from 2021 to 2023, Bitcoin network hash rates, and machine-learning price forecasts to arrive at the profit estimates.

What carries the argument

The load-bearing calculation is the daily mining revenue formula: revenue = predicted Bitcoin price × block reward (6.25 BTC) × (fleet hash rate / network hash rate) × 144 blocks per day. The fleet size is derived from monthly surplus electricity in kWh, reduced by a 3.59% transmission-loss rate, and divided by the power draw of the Antminer S21 XP Hyd (5,676 W). Bitcoin price enters through two machine-learning forecasts, Random Forest and LSTM, and hardware cost is handled through a 7.5-year straight-line depreciation schedule.

What would settle it

Use hourly or sub-daily solar surplus output from KEPCO's net-metered households (or the Jeju project) to compute how many hours per day the fleet can actually run; if the result is materially below 24 hours, the 30,565-miner base case and its $294 million profit figure collapse unless storage costs are added.

Watch

Extended reading notes

Core claim

The central claim is that otherwise-wasted solar surplus in South Korea can be converted into a significant national revenue stream through Bitcoin mining. The paper computes the daily Bitcoin harvest as the product of the block reward (6.25 BTC), the miner fleet's share of the total network hash rate, and 144 blocks per day, then multiplies that by the Bitcoin price predicted by Random Forest or LSTM models. Across four cases (two price models and two fleet sizes: a maximum of 45,439 miners and a fixed 30,565-miner fleet), the lowest 12-month profit is about $294 million and the highest is about $348 million, after straight-line depreciation of the mining hardware. The paper presents this as the first empirical feasibility study connecting South Korean electricity surplus with Bitcoin mining, and concludes that the revenue could materially help KEPCO's finances, provided legal barriers are lifted.

Load-bearing premise

The paper assumes surplus electricity is available to miners 24 hours a day, but household solar surplus is produced mainly in daylight and varies by season, so without storage the miner fleet cannot run around the clock.

Editorial extensions

If this is right

If the estimate holds, KEPCO could earn roughly $24.5 million per month in the most conservative case (Random Forest price predictions with a fixed 30,565-miner fleet).
The revenue stream would convert currently wasted solar surplus into an asset that can be applied to KEPCO's debt and unpaid settlement obligations.
The strategy requires amending KEPCO Act Article 13, which currently restricts KEPCO to electricity sales, so legislative change is a precondition for implementation.
The approach is portable to other renewables and to any region with net-metering surplus, not just South Korea.
Both price-prediction models and both fleet sizes produce positive profit, so the conclusion is not sensitive to the choice of forecasting model or fleet configuration.

Reading between the lines

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

If the 24-hour surplus availability assumption is relaxed to daylight-only generation, the miner fleet would need storage or grid backup; adding storage costs would erode the reported margins.
The reported profits assume network hash rate and block reward stay at historical levels; a large rise in network hash rate or the April 2024 halving to 3.125 BTC per block would shrink the daily BTC harvest, so the 2023-based estimates likely overstate future revenue.
The same fleet-sizing framework could be stress-tested against hourly solar generation data or applied to wind and hydroelectric surplus; if profitable under those conditions, the national-strategy case would strengthen considerably.

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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

Dear colleague,

Quick take: the paper is an honest, clearly written application of a standard mining-profit calculation to South Korean household-solar surplus data. It is the first to combine KEPCO's net-metering surplus figures with Bitcoin network hash rate and a current miner's specs. That data combination is the real novelty. The authors also deserve credit for stating their assumptions plainly, listing excluded costs, and acknowledging that the KEPCO Act currently blocks this use of surplus power.

But the headline profitability, $294–348 million per year, rests on an assumption the paper itself states: surplus power is available 24 hours per day. It is not. The surplus comes from household solar, so it is daylight-only without storage. The paper uses monthly kWh, divides by 24 hours, and derives miner counts and daily revenue as if the machines run around the clock. If real operating time is closer to 6 hours/day, revenue falls by a factor of about four, while depreciation stays fixed. The profit numbers in Table 9 shrink to roughly $75–100 million in revenue against $41–62 million in cost, i.e., near zero or negative before accounting for the costs the paper explicitly excluded (cooling, networking, labor, provider compensation). This is not a minor modeling choice; it is the mechanism that makes the headline plausible.

The price-prediction section has a similar problem. The technical features (Momentum = P_today − P_{t−N}, K%, D%, RSI) are all constructed from the same day's price, so the 'predicted' price is mostly a transformation of the target. The reported R² of 0.91 on the test period does not demonstrate forecasting ability; it demonstrates in-sample smoothing. A true out-of-sample test would use lagged features only.

There are also small slips: Table 9's cost calculation uses 40,439 miners, while the text and Table 7 say 45,439. That looks like a typo, but it should be caught.

Overall, the framework is clear and the data are real, but the central claim as stated is not supported. The paper is worth a serious referee, because the policy question is live and the dataset is unique, but it needs major revision: a realistic availability factor, explicit storage and grid-backup costs, and a genuine forecast evaluation. I would send it out rather than desk-reject, and the referee report should be specific about the availability assumption.

For your own work, I wouldn't cite it yet, but it could be a useful classroom example of load-bearing assumptions in energy economics.

Yours,

Referee Report

4 major / 4 minor

Summary. The paper proposes using surplus electricity from household solar net-metering in South Korea to operate Bitcoin miners, with KEPCO as the assumed operator. The authors estimate monthly miner counts from three years of KEPCO surplus data, use the Antminer S21 XP Hyd specifications and network hash rates to compute expected Bitcoin mined per day, and generate 2023 Bitcoin price predictions with Random Forest and LSTM models. They compare 12-month profitability across two simulation fleets and report profits of $294–348M after depreciation, concluding that the strategy could generate significant revenue for KEPCO while alleviating its debt. The paper also acknowledges legal barriers in the KEPCO Act and lists several cost items that are excluded from the analysis.

Significance. If the quantitative analysis were sound, the paper would be one of the first empirical case studies of a national Bitcoin-mining strategy built on solar surplus, with clear policy relevance for KEPCO and for utilities in other countries with net-metering surpluses. The authors deserve credit for using public data, presenting an explicit cost table, including an actual-price baseline, and acknowledging the legal obstacles. However, the central profitability estimates are not currently supported: the model assumes 24-hour availability of a daylight-only resource, the price 'predictions' are based on features that contain the same-day target price, and the cost model excludes major cost categories while being labeled conservative. Because all three issues are load-bearing for the headline numbers, the current manuscript does not establish the feasibility claim it advances.

major comments (4)

[Section 3, Assumption 2; Sections 4.2.2, 4.4; Table 9] The assumption that surplus power is available 24 hours per day contradicts the data source described in the paper: the surplus is residual household solar generation after net metering, and the paper itself lists energy storage as a future research item. This assumption is directly load-bearing because the monthly surplus kWh are converted into an all-day miner count, and the daily revenue formula multiplies by 144 blocks per day. Under realistic daylight-only availability (roughly 5–7 hours per day), either the same fleet operates about one quarter of the day, cutting mining revenue in Table 9 by roughly 75% while depreciation costs remain, or the fleet size must be quadrupled to consume the same energy, quadrupling the capital-cost line while leaving revenue approximately unchanged. Neither case reproduces the reported profits, and the difference can plausibly turn the remaining profit negative once excluded operating costs are added. The analysis should use hourly surplus profiles and, if storage is assumed, include storage costs and round-trip losses.
[Section 4.1.1–4.1.2; Table 4; Figure 5] The Bitcoin price 'predictions' are not genuine out-of-sample forecasts. The feature set in Table 4 includes Momentum with N = 1 (P_today minus the previous day's price), K%, D%, and RSI, all of which are computed using the current day's price P_today, which is exactly the target variable. The model can therefore recover the target from its own inputs, which explains the very high in-sample-style R² of 0.91 on the 2023 test set. This is label leakage, not forecasting skill, and it undermines the claim in Section 4.4 that the graphs indicate 'reliable forecasting.' The prediction exercise should be redone with strictly lagged features and a genuine recursive evaluation, or removed from the paper in favor of the actual-price baseline.
[Section 4.3; Table 8; Section 5] The cost model is not conservative, despite the claim in the conclusion. Table 8 excludes networking equipment, cooling systems, labor, maintenance and replacement costs, pool fees, facility costs, and any profit share or payment to the surplus electricity providers, and it also assumes zero electricity cost. Table 9 then reports profit as revenue minus only straight-line miner depreciation. For example, a fleet of 30,565 water-cooled miners would consume roughly 173 MW at full load, so the associated facility, cooling, networking, and staffing costs cannot be assumed to vanish. At minimum, these items should be quantified or, failing that, the results should be presented as upper bounds, not as a conservative estimate. The treatment of the surplus energy as free also needs an economic justification, since the electricity is generated by households under a net-metering arrangement.
[Section 4.4 and Table 9] No sensitivity or robustness analysis is provided for the two most consequential assumptions, the 24-hour availability factor and the zero-cost electricity. Table 9 reports only point estimates, yet the profit figures scale essentially linearly with the assumed daily operating hours and are highly sensitive to the excluded cost items. A minimal acceptable analysis would vary the availability factor between realistic daylight bounds and add a best- and worst-case operating-cost estimate, because the current results give no indication of how fragile the $294–348M headline profit range is.

minor comments (4)

[Section 4.5, cost calculation] The text says 'For Simulation 1, using 40,439 miners,' but the reported cost of approximately $62M corresponds to 45,439 miners, not 40,439; the number in Table 7 is 45,439.
[Section 2.1.2] The word 'tress' in the sentence about decision trees appears to be a typo for 'trees.'
[Section 5 and Figure 12] The labels '45.439' and '30.565' in the workflow figure use a decimal point where a thousands separator is intended; they should read '45,439' and '30,565.'
[Reproducibility] The exact formula used to convert monthly surplus kWh into the number of operational miners is not shown in the text, and no data or code availability statement is provided; this makes it difficult to audit the central calculation.

Circularity Check

1 steps flagged · score 6.0 of 10

Bitcoin price 'prediction' is circular because the features contain the same-day target price; profitability retains an independent actual-price baseline.

self definitional [Section 3.3, Table 4; applied in Section 4.1]
"Momentum: Ptoday − PN days ago; Pi is the price on day i and N = 1 in this analysis. ... K%: (Ptoday − L14)/(H14 − L14) × 100, where H14 is the highest price over the past 14 days and L14 is the lowest price over the past 14 days. D%: 3-day simple moving average of K%. RSI: 100 − 100/(1+RS), where RS = Average Gain over N days / Average Loss over N days."

These independent variables are all functions of Ptoday, the same-day Bitcoin price that is also the target variable the Random Forest and LSTM models are trained to predict. At test time (2023), the feature vector therefore contains the actual outcome, so the model output is largely a transformation of the target rather than an independent forecast. This explains the reported R2 of 0.91 (Random Forest) and 0.85 (LSTM) and the close tracking in Fig. 5. The 'predicted' prices entering the revenue formula are thus not genuinely out-of-sample predictions. However, the paper also includes an 'Actual Price' baseline row in Table 9, so the central profitability conclusion does not rest solely on the circular model outputs.

full rationale

The clearest circular step is the construction of the Bitcoin price predictors. Table 4 defines each feature using Ptoday (current-day price), which is exactly the quantity being predicted in Section 4.1. The Random Forest and LSTM 'predictions' are therefore partly self-referential: the high R2 and near-perfect visual fit are expected when the target is included as a feature. This is a genuine case of self-definitional leakage in the price-forecasting component. That said, the paper's headline profitability claim (Sections 4.5 and 5) is not wholly forced by this circularity, because Table 9 also reports profits computed from actual 2023 Bitcoin prices, independent of the ML models. Those actual-price rows still show positive profits under the paper's assumptions. The other major vulnerability, Assumption 2 (24-hour availability of surplus solar power), is an unrealistic or contestable assumption rather than a circular reduction, so it is not counted here as a circularity step. There is no load-bearing self-citation chain or imported uniqueness theorem. Overall, the circularity is partial: it undermines the model-based price 'predictions' and the model-dependent profit variations, but the central profitability estimate retains independent grounding via the actual-price baseline. Score 6 reflects that one central predictive component reduces by construction while the main result still has some independent content.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The revenue formula itself is standard Bitcoin mining arithmetic. The load-bearing content comes from the assumptions: free and continuous surplus power, hardware specs given by the vendor, and ML price predictions treated as forecasts. None of these is independently verified within the paper, and the 24-hour surplus assumption is contradicted by the seasonality of solar data shown in the paper itself.

free parameters (3)

Daily availability factor for surplus electricity = assumed 100% (24 h/day)
Section 3, Assumption 2. Converts monthly surplus kWh into 24 h/day mining capacity; this directly scales the miner count and revenue and is not matched to the solar generation profile.
Miner operating lifespan = 7.5 years / 90 months
Section 4.3. Straight-line depreciation over 90 months sets the per-miner cost at about $1,355 per year; a shorter lifespan would raise costs and lower profit.
ML model configurations = LSTM 20 epochs; RF and LSTM hyperparameters otherwise unreported
Section 4.1.1. The predicted price feeds directly into revenue, so the ad hoc and unreported model settings affect the headline profit numbers.

assumptions (6)

domain assumption A Bitcoin block is mined every 10 minutes on average, giving 144 blocks per day.
Section 3, Assumption 3, and Section 4.4 use 144 as a deterministic constant in the revenue formula.
domain assumption The Antminer S21 XP Hyd specifications (473 TH/s, 5,676 W, $10,165 unit price) are accurate and representative.
Section 4.2.1 takes these values from the BITMAIN product page, and they determine both the hash-rate share and the energy demand of the mining fleet.
ad hoc to paper Surplus electricity after net metering is available to KEPCO free of charge.
The cost model in Section 4.3 includes no electricity price, PPA, or payment to prosumers, and explicitly lists profit share for surplus electricity providers as an excluded item, so electricity is implicitly priced at zero.
ad hoc to paper Surplus power can supply miners 24 hours per day.
Section 3, Assumption 2 asserts this directly, but solar surplus is generated during daylight and varies seasonally, as shown in the paper's own monthly chart in Figure 4.
domain assumption Historical hash rate and historical price-derived indicators are sufficient to estimate mining revenue for the analysis period.
Section 4.4 uses actual 2023 hash rates and predicted prices, with no difficulty adjustment, no price uncertainty model, and no sensitivity analysis for the hash rate.
domain assumption The Random Forest and LSTM price predictions are legitimate forward forecasts.
Section 3.3 constructs features such as Momentum, K%, D%, and RSI using the same-day price as the target, so the test-period predictions are contemporaneous regressions rather than demonstrated out-of-sample forecasts.

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Cite this review

Pith. "Pith review of Leveraging Surplus Electricity: Profitability of Bitcoin Mining as a National Strategy in South Korea." pith.science (2026). https://pith.science/paper/JEJPOWPK

@misc{pith2026250500303,
  author       = {Pith},
  title        = {Pith review of: Leveraging Surplus Electricity: Profitability of Bitcoin Mining as a National Strategy in South Korea},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JEJPOWPK}},
  note         = {Machine review of arXiv:2505.00303}
}

read the original abstract

This study examines the feasibility and profitability of utilizing surplus electricity for Bitcoin mining. Surplus electricity refers to the remaining electricity after net metering, which can be repurposed for Bitcoin mining to improve Korea Electric Power Corporation's (KEPCO) energy resource efficiency and alleviate its debt challenges. Net metering (or net energy metering) is an electricity billing mechanism that allows consumers who generate some or all of their own electricity to use that electricity when they want, rather than when it is produced. Using the latest Bitcoin miner, the Antminer S21 XP Hyd, the study evaluates daily Bitcoin mining when operating at 30,565 and 45,439 units, incorporating Bitcoin network hash rates to assess profitability. To examine profitability, the Random Forest Regressor and Long Short-Term Memory models were used to predict the Bitcoin price. The analysis shows that the use of excess electricity for Bitcoin mining not only generates economic revenue, but also minimizes energy loss, reduces debt, and resolves unsettled payment issues for KEPCO. This study empirically investigates and analyzes the integration of electricity surplus in South Korea with bitcoin mining for the first time. The findings highlight the potential to strengthen the financial stability of KEPCO and demonstrate the feasibility of Bitcoin mining. In addition, this research serves as a foundational resource for future advancements in the Bitcoin mining industry and the efficient use of energy resources.

Figures

Figures reproduced from arXiv: 2505.00303 by the authors.

**Figure 1.** illustrates the internal structure of an LSTM cell, highlighting the cell state (depicted as a horizontal conveyor belt) and the gate that modulate the flow of information [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

**Figure 3.** Average number of households and average sur [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Monthly total surplus power over three years [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

Figures from the paper (8 more)

**Figure 2.** Figure 2: Bitcoin Price Data Over the Entire Period [PITH_FULL_IMAGE:figures/full_fig_p005_2.png]

**Figure 6.** Figure 6: Bitcoin Miner S21 XP Hyd. [21] [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 5.** Figure 5: Comparison the predicted Bitcoin prices with [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 8.** Figure 8: Expected Daily Number of Bitcoins Mined (By [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 11.** Figure 11: Daily Mining Profit (Before Deducting Costs) - [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]

**Figure 9.** Figure 9: Daily Mining Profit (Before Deducting Costs) - [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

**Figure 10.** Figure 10: Daily Mining Profit (Before Deducting Costs) - [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]

**Figure 12.** Figure 12: Analysis workflow The profitability analysis shows that utilizing surplus electricity for Bitcoin mining can minimize power losses, mitigate KEPCO’s unsettled payment issues, and generate economic profit. This represents the approach to converting unused electricity r…

Discussion (0). Continue with ORCID to comment.

Reference graph

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