"""Core services for orchestrating building energy analytics workflows (moved)."""
from better_lbnl_os.core.changepoint import ChangePointModelResult
from better_lbnl_os.core.recommendations import EEMeasureRecommendation
from better_lbnl_os.core.savings import SavingsEstimate
from better_lbnl_os.models import (
BuildingData,
UtilityBillData,
WeatherData,
)
from better_lbnl_os.models.benchmarking import BenchmarkResult
[docs]
class BuildingAnalyticsService:
"""Service for orchestrating building energy analysis workflows."""
[docs]
def analyze_building(
self,
building: BuildingData,
utility_bills: list[UtilityBillData],
weather_data: list[WeatherData],
) -> dict:
# Placeholder implementation
return {
"status": "success",
"building_id": building.name,
"message": "Analysis service to be implemented",
}
[docs]
def fit_models(
self,
building: BuildingData,
utility_bills: list[UtilityBillData],
weather_data: list[WeatherData],
) -> list[ChangePointModelResult]:
# Placeholder - will integrate with core algorithms
return []
[docs]
def benchmark_building(
self,
building: BuildingData,
model_results: list[ChangePointModelResult],
) -> BenchmarkResult:
# Placeholder - will implement benchmarking logic
return BenchmarkResult(
building_id=building.name,
percentile=50.0,
z_score=0.0,
rating="Average",
target_eui=100.0,
median_eui=100.0,
)
[docs]
def estimate_savings(
self,
building: BuildingData,
benchmark_result: BenchmarkResult,
utility_bills: list[UtilityBillData],
) -> SavingsEstimate:
# Placeholder - will implement savings calculation
return SavingsEstimate(
energy_savings_kwh=10000.0,
cost_savings_usd=1000.0,
emissions_savings_kg_co2=5000.0,
percent_reduction=15.0,
)
[docs]
def recommend_measures(
self,
building: BuildingData,
model_results: list[ChangePointModelResult],
benchmark_result: BenchmarkResult,
) -> list[EEMeasureRecommendation]:
# Placeholder - will implement recommendation engine
return []
[docs]
class PortfolioBenchmarkService:
"""Service for portfolio-level benchmarking and analysis."""
[docs]
def __init__(self):
self.buildings: list[BuildingData] = []
self.results: list[BenchmarkResult] = []
[docs]
def add_building(self, building: BuildingData, benchmark_result: BenchmarkResult) -> None:
self.buildings.append(building)
self.results.append(benchmark_result)
[docs]
def calculate_portfolio_metrics(self) -> dict:
if not self.results:
return {"status": "error", "message": "No buildings in portfolio"}
avg_percentile = sum(r.percentile for r in self.results) / len(self.results)
rating_counts: dict[str, int] = {}
for result in self.results:
rating_counts[result.rating] = rating_counts.get(result.rating, 0) + 1
return {
"total_buildings": len(self.buildings),
"average_percentile": avg_percentile,
"rating_distribution": rating_counts,
}
[docs]
def identify_improvement_targets(self, top_n: int = 10) -> list[str]:
sorted_results = sorted(self.results, key=lambda r: r.percentile, reverse=True)
return [r.building_id for r in sorted_results[:top_n]]
[docs]
def generate_portfolio_report(self) -> dict:
metrics = self.calculate_portfolio_metrics()
targets = self.identify_improvement_targets()
return {
"metrics": metrics,
"improvement_targets": targets,
"report_date": "2025-01-21",
}