WattCouncil: Governance in Synthetic Energy Modeling

• WattCouncil is a governed, multi-agent LLM framework that generates synthetic household electricity-demand scenarios with explicit cultural, temporal, and physical constraints.
• It employs a staged pipeline with role-specific agents for schema validation, auditing, and bounded regeneration to ensure robust scenario generation.
• Empirical evaluations reveal realistic temporal patterns but significant magnitude discrepancies, highlighting challenges in physical calibration.

Searching arXiv for the named paper and closely related work on WattCouncil-style energy governance and LLM/energy systems. First, locating the primary "WattCouncil" paper on arXiv. Now checking related arXiv papers that explicitly frame energy-aware planning or governance in “WattCouncil”-style terms. WattCouncil is a governed, multi-agent LLM framework for generating synthetic household electricity-demand scenarios under explicit cultural, temporal, and physical constraints. It is designed for smart-grid and low-carbon power-system research settings in which granular residential demand data are operationally valuable but difficult to access because of privacy concerns, regulatory barriers, collection cost, sparse instrumentation, and dataset fragmentation. Rather than forecasting actual households, WattCouncil produces context-aware synthetic scenarios by generating structured household descriptions, conditioning them on weather, and synthesizing daily electricity-consumption profiles through a staged pipeline with auditing, bounded regeneration, and schema validation (Takrouri et al., 12 Jul 2026).

1. Problem setting and analytical purpose

WattCouncil is motivated by the increasing dependence of low-carbon electricity systems on fine-grained residential demand characterization. The paper situates this need in the context of behind-the-meter technologies such as rooftop solar and electric vehicles, together with the broader heterogeneity of household demand arising from demographics, occupancy, culture, season, income, appliance use, and family dynamics. In that framing, household demand is not treated as a stationary signal but as a context-sensitive process requiring explicit conditioning variables rather than purely historical extrapolation (Takrouri et al., 12 Jul 2026).

The framework is presented as a response to limitations in three broad classes of existing practice. First, real high-resolution household datasets are scarce and sensitive. Second, rigid simulators can be difficult to adapt to diverse social and cultural routines. Third, purely statistical or data-driven methods remain constrained by the support of the historical data on which they were trained. WattCouncil therefore occupies an intermediate position: it is neither a bottom-up physical simulator nor a black-box time-series predictor. Its central objective is controlled scenario generation for benchmarking, exploratory analysis, and privacy-preserving downstream research.

The paper’s evaluation protocol reflects that objective. The framework is assessed against the CER Electricity Data (Revised March 2012), which contains half-hourly electricity consumption measurements over more than 500 days, from July 14, 2009 to December 31, 2010, for 4,232 residential customers and 529 SMEs, together with survey-based socio-demographic and dwelling information. Only residential customers are used in the WattCouncil study (Takrouri et al., 12 Jul 2026).

2. Governed multi-agent architecture

WattCouncil’s defining feature is governance. The system uses role-specialized LLM agents inside a fixed pipeline rather than a single unconstrained generator. Each stage produces a schema-constrained JSON artifact, and continuation requires both schema validation and role-specific audit approval. The council roles are fixed and non-overlapping.

Role	Model and settings	Function
Generator	Gemini 2.5 Flash, $\tau = 0.7$, max tokens 16,000	Produces the structured candidate artifact
Cultural Auditor	Llama-4 Maverick 17B, $\tau = 0.3$, max tokens 4,000	Checks social, behavioral, and country-specific plausibility
Physical Auditor	Claude 4 Sonnet, $\tau = 0.3$, max tokens 4,000	Checks numerical consistency and physical plausibility
Editor	Llama 3.3 70B, $\tau = 0.4$, max tokens 16,000	Performs targeted correction after partial regeneration
Approver	Mistral Small 3.2, $\tau = 0.3$, max tokens 4,000	Independently verifies Editor corrections
Controller	Qwen2.5-72B-Instruct, $\tau = 0.4$, max tokens 4,000	Decides whether to accept or regenerate

The design is intentionally asymmetric. The Generator uses a higher temperature to promote diversity in proposals, whereas the auditing and control roles use lower temperatures to enforce precision and consistency. The paper gives the standard temperature-scaled softmax relation

$$p_i = \frac{\exp\left(z_i / \tau\right)}{\sum_{j=1}^{K} \exp\left(z_j / \tau\right)},$$

with lower $\tau$ yielding more deterministic behavior and higher $\tau$ yielding more diverse output (Takrouri et al., 12 Jul 2026).

Audit outputs are themselves structured JSON reports with severity labels LOW, MEDIUM, or HIGH. On the basis of the candidate artifact and those audit reports, the Controller chooses one of three actions: ACCEPT, REGENERATE PARTIAL, or REGENERATE FULL. Partial regeneration invokes the Editor, followed by independent verification by the Approver. The pipeline also supports a scoped rule memory for recurring failure patterns, with versioning and logging so that generations can be replayed under the same effective rule set. Reproducibility is further supported by fixed model-role assignment, pinned prompts and schemas, deterministic control logic, fixed inference settings, and logging of all intermediate artifacts (Takrouri et al., 12 Jul 2026).

3. Staged generation pipeline and contextual conditioning

The pipeline has three stages. Stage 1 performs household structure generation, including occupancy and work regime assignment. Stage 2 performs weather generation or ingestion. Stage 3 performs household electricity-consumption synthesis. Stage 2 is modular and can be replaced by external Typical Meteorological Year data without changing the remainder of the system, a choice motivated by the paper’s view that weather is highly constrained and physically difficult for general-purpose LLMs to generate reliably (Takrouri et al., 12 Jul 2026).

Conditioning variables are explicitly social, temporal, and environmental. Household composition includes the number of occupants, adults versus children, household type, house type, and work or occupancy regime. Temporal variables include season, hour of day, weekday versus weekend, and seasonal calendar assumptions. Cultural factors include country-specific social norms and narrative coherence around routines and activities. Environmental conditions include temperature, irradiance, and humidity. Physical plausibility rules include checks such as not activating heating under summer conditions, matching occupancy to realistic usage patterns, and ensuring environmental variables are numerically coherent (Takrouri et al., 12 Jul 2026).

Operationally, the framework reasons from family structure and contextual assumptions toward daily routines, from routines toward appliance use, and from appliance use toward a household load profile. The output is an hourly or half-hourly electricity-consumption profile over the day, later aggregated into seasonal average 24-hour profiles for comparison with real smart-meter data. The paper does not provide an explicit power-equation model or appliance-level conversion formula; the translation from routines to demand remains an LLM-driven structured synthesis governed by auditing and regeneration rather than a physical bottom-up simulator (Takrouri et al., 12 Jul 2026).

Weather handling illustrates the framework’s modularity. Typical Meteorological Year is defined as a synthetic weather year assembled from historically representative months for a given location, and the authors use the Python library pvlib to generate a full year of hourly weather variables for a location specified by latitude and longitude. In WattCouncil, TMY functions as a physically grounded substitute for direct LLM weather generation when tighter weather realism is required (Takrouri et al., 12 Jul 2026).

4. CER-based evaluation design

For conditioning and evaluation, the authors choose explicit survey-derived household groups from CER rather than latent clusters, arguing that cluster identifiers have limited semantic value for LLM prompting. Three survey variables are retained: household size, house type, and household composition. House types are encoded as apartment, semi-detached, detached, terraced, and bungalow. Household composition categories are single occupant, adults only, and adults with children under 15 years (Takrouri et al., 12 Jul 2026).

From the top 20 most frequent combinations, five representative groups are selected:

Group	Definition	Group size
1	1 person, bungalow, single occupant	171
2	2 people, terraced, adults only	173
3	3 people, semi-detached, adults with children	80
4	3 people, detached, adults only	90
5	4 people, detached, adults with children	113

These five groups account for 627 customers in the evaluation subset. The synthetic setup expands them into $5 \times 10 \times 4 \times 5 = 1000$ synthetic household profiles by combining 5 representative family groups, 10 employment or occupancy variants per group, 4 seasons, and 5 independent weather realizations per season (Takrouri et al., 12 Jul 2026).

Seasonal profile construction is also explicit. For real CER data, seasons are defined by Northern Hemisphere calendar months: winter as December to February, spring as March to May, summer as June to August, and autumn as September to November. Hourly consumption values are grouped by season and hour of day across the selected CER households. The synthetic data are aggregated in the same way, computing hourly means, standard deviations, and sample counts to produce representative 24-hour seasonal profiles (Takrouri et al., 12 Jul 2026).

The evaluation metrics are Pearson correlation coefficient for temporal shape similarity and peak-time alignment, together with MAE, RMSE, and MAPE for magnitude mismatch. The main realism test is therefore not household-by-household replication but the similarity of seasonal daily average shapes and variability (Takrouri et al., 12 Jul 2026).

5. Empirical findings, ablations, and runtime behavior

The principal empirical result is that WattCouncil reproduces daily temporal structure more successfully than absolute consumption scale. Across the five representative groups and four seasons, correlations range from 0.569 to 0.838, while MAPE remains high and increases with household size. Group 1 shows, for example, Corr values of 0.670 in winter, 0.611 in spring, 0.639 in summer, and 0.670 in autumn, with MAPE values from 62.07% to 72.76%. Group 5 shows Corr values of 0.726, 0.703, 0.643, and 0.749, with MAPE values from 91.03% to 93.98% (Takrouri et al., 12 Jul 2026).

The paper interprets those results as evidence that context-aware occupancy and activity modeling can recover realistic timing patterns, while the magnitude gap indicates missing physical determinants such as dwelling envelope, appliance power realism, HVAC efficiency, and simplified thermal-response constraints. A plausible implication is that WattCouncil’s current strengths lie in controllable scenario structure and temporal plausibility rather than calibrated end-use scaling.

Weather ablation is performed in two forms. First, Stage-2 LLM-generated weather is compared with TMY weather for Dublin, Ireland, assumed representative because exact household locations are unavailable and weather variance across Ireland is treated as low. Pearson correlations between LLM and TMY weather profiles are reported by season and variable. In winter, the values are 0.724 for temperature, 0.933 for DNI, 0.961 for DHI, and 0.563 for relative humidity. In autumn, they are 0.547, 0.930, 0.994, and 0.428 respectively. The authors conclude that direct weather generation by general-purpose LLMs introduces avoidable inaccuracies, especially for humidity and some seasonal temperature patterns, and therefore recommend external weather sources or specialized climate models when possible (Takrouri et al., 12 Jul 2026).

Second, the study compares downstream household electricity profiles under LLM weather versus TMY weather for a fixed household: Ireland; adults with children under 15 years; detached house; four occupants; Father (office), Mother (hybrid), Son (student), Daughter (student); mixed work regime; partial weekday daytime occupancy; employee chief income earner; and appliances including washing machine, dishwasher, oven, refrigerator, television, and microwave. Pearson correlations between the resulting demand profiles range approximately from 0.74 to 0.98. The paper’s interpretation is that overall demand structure is robust to weather source, while differences appear more strongly in uncertainty bands than in mean shape. It also notes muted summer demand and attributes it to governance constraints that repeatedly disable implausible cooling or heating assignments together with conservative behavioral assumptions (Takrouri et al., 12 Jul 2026).

The governance mechanism is illustrated with an end-to-end example in which one synthetic household is processed through all stages under one weather condition across four seasons, with weekday and weekend variants, yielding eight independent Stage-3 runs. Every detected violation triggered full regeneration, so the Editor and Approver were never used. A concrete summer weekday candidate was rejected because heating was active under summer conditions, and a regenerated version was accepted. Runtime statistics for that example show 52 total model calls and 556.8 seconds overall, comprising 14 Generator calls, 10 Cultural Auditor calls, 14 Physical Auditor calls, and 14 Controller calls, with 190,110 input tokens and 54,775 output tokens (Takrouri et al., 12 Jul 2026).

6. Significance, limitations, and related uses of the term

WattCouncil’s stated contributions are fivefold: a multi-agent LLM-based framework for governed synthetic energy-data generation, a staged pipeline with explicit auditing and bounded regeneration, an evaluation protocol based on similarity to real smart-meter data, ablation studies on key pipeline components, and open-access code (Takrouri et al., 12 Jul 2026). Its methodological novelty lies less in a new predictive model than in a governance architecture for synthetic data generation: explicit roles, schema-constrained intermediate artifacts, independent auditing, bounded regeneration, modular component replacement, and conditioning on semantically meaningful household context.

The framework’s limitations are explicit. Validation is restricted to Irish households because CER was the only sufficiently rich dataset available. Similarity is measured at aggregated seasonal and hourly level, so household-level realism remains less certain. Only three CER metadata attributes are used for conditioning. Weather is treated as uniform across Ireland. The system offers no formal privacy guarantee such as differential privacy. Governance adds substantial computational overhead. Most importantly, the current system still exhibits a substantial magnitude gap relative to real loads, which the authors attribute to missing physical determinants such as dwelling envelope properties, HVAC efficiency, appliance power ratings, and simplified thermal-response constraints (Takrouri et al., 12 Jul 2026).

In related literature, “WattCouncil” also appears as a broader decision-support perspective for energy-aware digital infrastructure rather than only as the name of this household-scenario framework. The analytical study of context-length routing for LLM inference uses the term to frame optimization around tokens per watt and routing topology (Chen et al., 18 Mar 2026). “WattGPU” positions itself as decision support for choosing the right GPU for a target LLM and workload without exhaustive profiling (Argerich et al., 2 Jul 2026). Work on Kubernetes observability uses the phrase in connection with workload-level energy accountability and governance (Pijnacker et al., 14 Apr 2025). Heterogeneous GPU benchmarking for LLM inference similarly emphasizes hardware-aware energy governance and scenario-specific placement decisions (Argerich et al., 10 Apr 2026). This suggests that WattCouncil can be read in two linked senses: as a specific governed synthetic-data generator for household electricity demand, and as a broader idiom for explicit, auditable, energy-aware decision-making across power-system and computing domains.