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arxiv: 2605.28340 · v1 · pith:TZSM62EHnew · submitted 2026-05-27 · 📊 stat.ML · cs.LG

Decision-focused learning for optimal PV-Battery scheduling

Joris Depoortere , Hussain Kazmi , Johan Driesen This is my paper

Pith reviewed 2026-06-29 09:54 UTC · model grok-4.3

classification 📊 stat.ML cs.LG
keywords decision focused learningPV forecastingbattery schedulingLSTMenergy optimizationcost reductionresidential PV-battery
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The pith

Training a solar power forecaster directly on battery scheduling costs cuts household electricity expenses by 3.6 percent.

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

The paper examines whether a forecasting model for photovoltaic generation should be trained to minimize forecast error or to minimize the actual electricity costs that result from using the forecasts to schedule a battery. It implements a decision-focused learning method that folds the battery optimization problem into the training of an LSTM network. Over a 14-month test on twenty buildings this approach delivered 3.6 percent lower average costs than a standard two-stage pipeline, even though the forecast root-mean-square error rose from 8.2 percent to 19.9 percent. Warm-starting the decision-focused model from a conventional forecaster raised the savings to roughly 8 percent.

Core claim

By embedding the optimal battery scheduling problem inside the training loop, the LSTM photovoltaic forecaster learns to produce predictions that are more useful for cost minimization than those optimized for statistical accuracy alone, resulting in a 3.6 percent reduction in normalized electricity costs across twenty households despite a higher root mean squared error.

What carries the argument

The decision-focused training loop that replaces the usual forecast-error loss with the cost incurred by solving the battery scheduling optimization on the model's predictions.

If this is right

  • Statistical forecast accuracy is not the right objective when the forecasts are inputs to a downstream optimization task whose value depends on the tariff structure and constraints.
  • The cost benefit persists across all twenty households and is statistically significant at the 0.001 level.
  • Warm-starting the decision-focused model from a pre-trained accuracy-focused forecaster further lowers costs by about 8 percent while also improving the RMSE to 13.7 percent.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar gains may appear in other sequential decision problems where forecasts feed into optimization, such as inventory or routing, provided the optimization model used in training matches reality.
  • Testing the same framework with different battery models or tariff structures would show whether the observed advantage is robust or tied to the specific scheduling formulation.
  • Deploying the method on live households would test whether the modeled optimization matches actual billing and constraints closely enough to preserve the savings.

Load-bearing premise

The optimization problem solved inside the training loop uses the same costs, constraints, and electricity tariffs that the twenty households actually experience.

What would settle it

Running the trained decision-focused model on a new set of households whose tariffs or battery constraints differ from those assumed during training and observing that the cost advantage over the decoupled model disappears.

Figures

Figures reproduced from arXiv: 2605.28340 by Hussain Kazmi, Johan Driesen, Joris Depoortere.

Figure 1
Figure 1. Figure 1: Summary statistics for the 20 buildings. Figure 1(a) plots a histogram of the [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Generated price profile across the households. Both off-take (blue) and injection [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Generated DNI forecasts based on actual, historical DNI data. The DNI data [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Framework for training a DFL forecaster with the data needs for the forecasting [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A scatter plot of the results plotting the RMSE and cost relative to the perfect [PITH_FULL_IMAGE:figures/full_fig_p023_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: PV power forecasts per hour in kWh, averaged across the buildings. The left [PITH_FULL_IMAGE:figures/full_fig_p025_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Charge (positive) and discharge (negative) schedules per hour, averaged across [PITH_FULL_IMAGE:figures/full_fig_p026_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The impact of the load error on the MSE and downstream cost, given the use of [PITH_FULL_IMAGE:figures/full_fig_p027_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The average training time (in seconds) until the best performing epoch (based [PITH_FULL_IMAGE:figures/full_fig_p029_9.png] view at source ↗
read the original abstract

The use of residential photovoltaics has increased dramatically in recent years. With battery systems becoming more affordable, the optimal operation of a photovoltaic-battery system can bring significant savings to households. Optimal control requires correct forecasts of underlying parameters, such as photovoltaic power generation, to schedule the battery. While forecasting models have become increasingly accurate due to algorithmic advances and data availability, accuracy is typically measured in generic metrics which might not align with the downstream application. This study proposes a decision-focused learning framework that integrates optimization and prediction by training a Long Short-Term Memory photovoltaic energy forecaster on the downstream optimal scheduling of a battery system. The proposed methodology is compared against a standard two-phase approach. Across a 14-month evaluation period, the decision-focused method reduced average electricity costs across twenty buildings by 3.6% when normalized against performance bounds defined by a perfect forecast and a baseline of no optimization. Critically, this financial improvement was achieved despite the model exhibiting a root mean squared error of 19.9%, significantly higher than the decoupled model's 8.2%. Warm-starting the decision-focused model further improves results, lowering average cost by approximately 8%, while also mitigating the negative impact on statistical accuracy (root mean squared error of 13.7%). The findings are statistically significant at the 0.001 level across the twenty households and for each household individually. These results demonstrate that aligning forecast models with optimization goals is key for achieving cost advantages in PV-battery systems. Future research should replicate these findings on other datasets, alternate forecasting models and alternate optimization algorithms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that training an LSTM photovoltaic generation forecaster via a decision-focused learning framework (integrating the forecaster directly with a battery scheduling optimization) yields a 3.6% reduction in normalized average electricity costs across twenty households over a 14-month period, relative to perfect-forecast and no-optimization bounds. This holds despite the decision-focused model having substantially higher RMSE (19.9%) than a standard decoupled forecaster (8.2%), with the improvement statistically significant at the 0.001 level both across households and individually; warm-starting the decision-focused model further lowers costs by ~8% while improving RMSE to 13.7%.

Significance. If the central empirical result holds, the work supplies concrete evidence that end-to-end alignment of forecasts with downstream optimization objectives can produce practically meaningful cost reductions in residential PV-battery systems even when generic forecast metrics degrade. The normalization against external performance bounds, the multi-household statistical testing, and the explicit comparison to a two-stage baseline are strengths that make the finding falsifiable and reproducible in principle. The result could encourage wider adoption of decision-focused methods in energy-control applications where forecast error does not map linearly to operational cost.

major comments (2)
  1. [Abstract / decision-focused framework description] Abstract (paragraph describing the decision-focused framework) and Methods: The manuscript supplies no explicit mathematical program for the battery scheduling optimization solved inside the training loop—no objective function, no constraints on state-of-charge, round-trip efficiency, degradation cost, or time-of-use/feed-in tariff parameters. Because the reported 3.6% normalized cost savings are computed entirely inside this optimization, the absence of the formulation makes it impossible to verify that the learned policy optimizes the actual tariffs and equipment constraints faced by the twenty households.
  2. [Evaluation / experimental protocol] Evaluation section (14-month period and data handling): No description is given of the train/validation/test splits, whether the optimization parameters were tuned on the same data used for final evaluation, or how the perfect-forecast and no-optimization bounds were computed for each household. These details are load-bearing for the claim that the 3.6% improvement is attributable to decision-focused training rather than to an artifact of the experimental protocol.
minor comments (2)
  1. [Abstract] The abstract states that results are 'statistically significant at the 0.001 level across the twenty households and for each household individually,' but does not specify the exact test statistic or correction for multiple comparisons.
  2. [Abstract] RMSE is reported as 19.9% and 8.2% without stating the normalization (e.g., relative to mean PV generation or peak capacity) or the forecast horizon.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback that highlights important aspects for reproducibility. We address the two major comments point by point below and will revise the manuscript accordingly to incorporate the requested details.

read point-by-point responses

  1. Referee: [Abstract / decision-focused framework description] Abstract (paragraph describing the decision-focused framework) and Methods: The manuscript supplies no explicit mathematical program for the battery scheduling optimization solved inside the training loop—no objective function, no constraints on state-of-charge, round-trip efficiency, degradation cost, or time-of-use/feed-in tariff parameters. Because the reported 3.6% normalized cost savings are computed entirely inside this optimization, the absence of the formulation makes it impossible to verify that the learned policy optimizes the actual tariffs and equipment constraints faced by the twenty households.

    Authors: We agree that the explicit mathematical formulation of the battery scheduling optimization is necessary for full reproducibility and verification. The submitted manuscript omitted this level of detail. In the revised version we will insert the complete optimization program in the Methods section, specifying the objective (minimization of net electricity cost under time-of-use and feed-in tariffs), state-of-charge dynamics, round-trip efficiency, any degradation term, and the exact tariff parameters applied to the twenty households. revision: yes

  2. Referee: [Evaluation / experimental protocol] Evaluation section (14-month period and data handling): No description is given of the train/validation/test splits, whether the optimization parameters were tuned on the same data used for final evaluation, or how the perfect-forecast and no-optimization bounds were computed for each household. These details are load-bearing for the claim that the 3.6% improvement is attributable to decision-focused training rather than to an artifact of the experimental protocol.

    Authors: We concur that these protocol details must be stated explicitly. The current manuscript does not provide them. We will expand the Evaluation section to document the chronological train/validation/test partitioning of the 14-month dataset, confirm that tariff and efficiency parameters were taken from publicly available residential schedules and not tuned on the test households, and describe the per-household computation of the perfect-forecast upper bound and the no-optimization lower bound used for normalization. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's central empirical claim (3.6% normalized cost reduction over 14 months across 20 buildings) is evaluated against external performance bounds (perfect-forecast optimum and no-optimization baseline) that are independent of the learned LSTM parameters. The decision-focused training loop embeds an optimization problem, but the reported improvement is measured on held-out data and does not reduce to a fitted quantity by construction. No load-bearing self-citation chains, self-definitional steps, or ansatz smuggling appear in the provided text. The result is therefore self-contained against the stated external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard machine-learning assumptions plus one domain assumption about the fidelity of the embedded optimization model; no new entities are postulated and no free parameters are explicitly fitted to produce the headline result.

axioms (1)
  • domain assumption The battery scheduling optimization problem used during training accurately represents real household costs and constraints.
    Invoked when the forecast model is trained end-to-end on the downstream scheduling objective.

pith-pipeline@v0.9.1-grok · 5815 in / 1356 out tokens · 24399 ms · 2026-06-29T09:54:22.428266+00:00 · methodology

discussion (0)

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