AI-Driven Microgeneration Management: Forecasting and Optimization

A mistake in a 15-minute interval can cost 500 rubles — over a month, that adds up to 15,000 rubles, and over a year, up to 180,000 rubles of pure loss. Owners of microgeneration face a dilemma: sell surplus to the grid at the night tariff or store it in a BESS for the morning peak. Without an accurate forecast, decisions are made intuitively — and lead to losses. Every day, you make up to 96 energy distribution decisions. A single error can negate the benefit of the rest.

We build AI systems that automatically manage microgeneration. Our algorithms account for weather forecasts, tariff curves, BESS state, and site load. The result: up to 30% savings on electricity costs and additional revenue from surplus sales. Get a consultation on implementing such a system for your site.

How an AI System Reduces Losses in Microgeneration Management

Generation Forecast with 15-Minute Accuracy

The microgenerator's task: every 15 minutes, decide whether to sell surplus, charge the BESS, or draw power from the grid. The decision depends on three factors:

• Forecast of own generation (solar, wind) for the next 24–48 hours
• Forecast of site consumption
• Tariff curve (time-of-use tariff: night/day/peak)

For forecasting, we use the Prophet model with additional regressors: GHI (global horizontal irradiance), temperature, cloud cover. This yields generation forecast accuracy of ±8% (MAPE) for the next day. In practice, you know 96% accurately how much energy you will get tomorrow. Compare forecasting methods:

The choice depends on data volume and required accuracy. For a typical site with 6–12 months of history, Prophet is optimal.

import numpy as np
import pandas as pd
from prophet import Prophet

class MicrogenerationForecaster:
    """Forecast generation and consumption for a microgeneration site"""

    def fit_solar_model(self, historical_generation, capacity_kw):
        """Forecast solar generation using Prophet"""
        df = historical_generation.reset_index()
        df.columns = ['ds', 'y']
        df['y'] = df['y'] / capacity_kw  # normalize to capacity factor

        model = Prophet(
            changepoint_prior_scale=0.05,
            seasonality_mode='multiplicative',
            yearly_seasonality=10,
            daily_seasonality=True,
            weekly_seasonality=False  # solar does not depend on day of week
        )
        model.fit(df)
        return model

    def predict_next_day(self, solar_model, load_model, weather_forecast):
        """Combined forecast for the next 24 hours"""
        future = solar_model.make_future_dataframe(periods=96, freq='15min')

        # Add weather regressor
        future = future.merge(weather_forecast[['ds', 'ghi', 'temperature']],
                             on='ds', how='left')

        solar_forecast = solar_model.predict(future)
        load_forecast = load_model.predict(future)

        return pd.DataFrame({
            'timestamp': future['ds'],
            'solar_kw': solar_forecast['yhat'].clip(0) * self.capacity_kw,
            'load_kw': load_forecast['yhat'].clip(0),
            'net_kw': (solar_forecast['yhat'] - load_forecast['yhat']).clip(-self.max_load)
        })

How to Optimize BESS with AI?

The charge/discharge strategy of the BESS is solved as a stochastic optimization problem with a 24–48 hour horizon. We use linear programming (LP). Variables: BESS charge/discharge power, grid purchase/sale. Criterion: minimize cost or maximize revenue. Typical constraints: capacity 10 kWh, max power 5 kW, depth of discharge 10–90%, efficiency 90%.

from scipy.optimize import linprog
import numpy as np

def optimize_bess_schedule(
    solar_forecast,   # kW per hour
    load_forecast,    # kW per hour
    tariff_grid_buy,  # RUB/kWh per hour (grid purchase)
    tariff_grid_sell, # RUB/kWh per hour (grid sale)
    bess_capacity_kwh=10,
    bess_max_power_kw=5,
    bess_soc_init=0.5,
    bess_soc_min=0.1,
    bess_soc_max=0.9,
    bess_efficiency=0.9
):
    """
    Optimize BESS schedule.
    Variables: [p_charge_t, p_discharge_t, p_grid_buy_t, p_grid_sell_t] × 24h
    """
    T = len(solar_forecast)

    # Linear programming (LP)
    # Simplified: not considering binary constraints for simultaneous charge/discharge
    # Variables: [charge[0..T], discharge[0..T], buy[0..T], sell[0..T], soc[0..T]]
    n_vars = T * 4 + T  # charge, discharge, buy, sell, SoC

    c = np.zeros(n_vars)
    # Minimize cost: purchase cost - sale revenue
    for t in range(T):
        c[2*T + t] = tariff_grid_buy[t]    # purchase — cost
        c[3*T + t] = -tariff_grid_sell[t]  # sale — revenue (negative)

    # Constraints (simplified)
    # SoC balance, power limits, SoC limits

    result = linprog(c, method='highs')  # HiGHS solver
    return result

AI optimization is 2-3 times more effective than manual control in cost savings. Compare:

Why Zero-Export Control Is Critical for Microgeneration?

If the grid sale tariff is unfavorable or unavailable, reverse power flow must be prevented. Zero-Export Control addresses this:

• Forecast surplus → increase load of controllable devices (water heater, pump, EV charging).
• If unable to absorb, curtail inverter output.
• Fast control loop (100 ms): measure current at point of connection → PID controller for inverter output power.

Aggregation for Demand Response Programs (VPP)

Hundreds of private microgenerators are aggregated into a virtual power plant (VPP). An AI aggregator:

• Forecasts total export capacity.
• Participates in the balancing market.
• Distributes payments among participants based on contribution (Shapley Values).

Monitoring and Diagnostics

Since the legalization of microgeneration, we have completed 15+ projects. Panel degradation assessment:

• Monitor Performance Ratio (PR): expected vs. actual generation.
• PR decline of 0.5%/year is normal; >1.5%/year indicates degradation or soiling.
• IV-curve analysis: identify shading, bypass diode failure.

What Is Included in the Development of an AI Microgeneration Management System

• Data analysis: collection and aggregation of historical generation, consumption, and tariff data. Check for gaps and outliers.
• Development of forecasting models: Prophet or LSTM for generation, ARIMA for consumption. Accuracy assessment via cross-validation.
• BESS optimizer setup: solve LP problem with actual BESS parameters and tariffs.
• Equipment integration: connect to inverters (Huawei, Sungrow, Victron) via Modbus/SunSpec, to BMS and meters.
• Testing on historical data: A/B experiment — compare AI decisions with actual data.
• Documentation and training: operator manual, algorithm description, model update schedule.
• 12-month warranty support: bug fixes, consultations, adaptation to tariff changes.

Development timeline: 2–4 months for a system with forecasting, BESS optimization, and Zero-Export control. Cost is calculated individually based on equipment complexity and data volume.

Our team has 6+ years of experience in AI for energy. Order a consultation — we will assess your microgeneration project in 2 days. Contact us to discuss implementation details.

Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing

We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.

Healthcare: Regulatory Maze and Data Governance

Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.

Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.

Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.

Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.

Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?

The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.

Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.

Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.

AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.

Retail and e‑commerce: Recommendation Systems and Demand Forecasting

Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.

Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.

Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.

Manufacturing: Quality Inspection and Predictive Maintenance

Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.

Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.

Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.

General Principles of Industry AI

Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.

We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.

Work Process for an Industry AI Solution

1. Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
2. MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
3. Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
4. Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
5. Support and monitoring — model drift, retraining, SLA.

Estimated timelines:

Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.

Why Choose Our Industry AI Solutions?

• 80+ completed projects in fintech, healthcare, retail, and manufacturing.
• 5 years on the market — proven experience with compliance and deployment.
• Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
• Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
• Flexibility: we work as a contractor or as an extension of your team.

Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.