Outage Prediction and Reliability Analytics

The Business Problem: Reducing the Impact of Outages

Outages are among the most visible and costly challenges for utilities. Severe weather, vegetation contact, equipment failures, and accidents can disrupt service, triggering widespread customer complaints, regulatory scrutiny, and financial penalties. For every minute the lights are out, reliability metrics such as SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index) worsen, directly influencing performance-based incentives and public perception.

Weather-related outages are especially disruptive. High winds bring down lines, ice accumulates on conductors, and storms knock trees into feeders. Vegetation is a leading cause of faults in distribution networks, particularly in storm-prone regions. When outages occur during major weather events, restoration becomes more difficult because crews face hazardous conditions and blocked access routes.

Traditionally, utilities have been reactive: storms strike, outages happen, and crews are dispatched. While vegetation management and equipment hardening programs help, they often follow fixed cycles or broad risk maps rather than precise, predictive targeting. This reactive posture leaves utilities vulnerable to both operational strain and customer frustration.

The Analytics Solution: Predicting Outages Before They Happen

Outage prediction uses data-driven analytics to estimate the likelihood of faults and disruptions before they occur. By combining weather forecasts, vegetation density maps, equipment condition data, and historical outage records, machine learning models can learn patterns that precede failures.

Classification models, for example, can estimate outage risk for each feeder or substation during an approaching storm, based on inputs such as forecast wind speed, rainfall, feeder vegetation exposure, and past performance under similar conditions. These predictions enable utilities to pre-stage crews where they are most likely to be needed, shorten restoration times, and optimize resource allocation.

Reliability analytics extends this approach over longer horizons. By analyzing multi-year outage histories alongside asset and environmental factors, utilities can identify systemic weaknesses—such as aging circuits that fail repeatedly in storms or areas with insufficient vegetation clearance. This informs capital planning, targeted hardening, and focused vegetation management programs that prevent outages rather than just reacting to them.

Operational and Financial Benefits

The benefits of predictive outage analytics are twofold: operational efficiency and improved reliability performance. Crew staging informed by risk models can dramatically cut restoration times by positioning resources ahead of an event. This reduces overtime costs and accelerates service restoration, improving customer satisfaction and regulatory scores.

Over the long term, data-driven reliability analytics supports smarter investments. Rather than blanket upgrades or broad vegetation trimming cycles, utilities can direct funds toward feeders and equipment with the highest risk and impact. This targeted approach maximizes return on investment and aligns reliability improvements with measurable outcomes.

These techniques are particularly valuable as climate change drives more extreme weather. Utilities face growing storm frequency and intensity, making proactive outage mitigation an essential part of resilience planning. Predictive models transform storm response from reactive dispatch to preemptive action, increasing grid resilience in a cost-effective manner.

Transition to the Demo

In this chapter’s demo, we will build a simplified outage prediction workflow. Using simulated data on weather, vegetation, and feeder attributes, we will:

• Train a classification model to estimate outage probability based on storm conditions and environmental risk factors.
• Generate feeder-level risk scores for a hypothetical weather event.
• Visualize predicted risk across a sample service territory to demonstrate how these insights guide crew staging and reliability planning.

This exercise shows how analytics can directly reduce outage impacts, improve restoration efficiency, and feed into long-term reliability strategies. By leveraging data utilities already collect, outage prediction moves reliability management from a reactive burden to a proactive capability that strengthens both grid resilience and customer trust.

Code

"""
Chapter 6: Outage Prediction and Reliability Analytics
Uses weather and asset exposure data to predict storm-driven outages.
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, roc_auc_score
from sklearn.inspection import permutation_importance

def generate_storm_outage_data(samples=1500):
    """
    Simulate weather events and outages for overhead distribution lines.
    Features: wind speed, rainfall, tree density, asset age.
    """
    np.random.seed(42)
    wind_speed = np.random.normal(20, 8, samples)   # m/s
    rainfall = np.random.normal(50, 20, samples)    # mm
    tree_density = np.random.uniform(0, 1, samples) # fraction canopy
    asset_age = np.random.uniform(1, 40, samples)   # years

    outage_prob = 1 / (1 + np.exp(-(0.15*(wind_speed-25) + 0.03*(rainfall-60) + 2*(tree_density-0.5))))
    outages = np.random.binomial(1, outage_prob)

    return pd.DataFrame({
        "WindSpeed_mps": wind_speed,
        "Rainfall_mm": rainfall,
        "TreeDensity": tree_density,
        "AssetAge_years": asset_age,
        "Outage": outages
    })

def train_outage_model(df):
    """
    Train Gradient Boosting classifier for outage prediction.
    """
    X = df[["WindSpeed_mps", "Rainfall_mm", "TreeDensity", "AssetAge_years"]]
    y = df["Outage"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)

    model = GradientBoostingClassifier(n_estimators=150, learning_rate=0.05, max_depth=3, random_state=42)
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]

    print("Outage Prediction Report:")
    print(classification_report(y_test, y_pred, target_names=["No Outage", "Outage"]))
    print(f"ROC AUC Score: {roc_auc_score(y_test, y_prob):.3f}")

    # Feature importance
    result = permutation_importance(model, X_test, y_test, n_repeats=10, random_state=42)
    importance_df = pd.DataFrame({
        "Feature": X.columns,
        "Importance": result.importances_mean
    }).sort_values("Importance", ascending=True)

    plt.figure(figsize=(8, 5))
    plt.barh(importance_df["Feature"], importance_df["Importance"], color="black")
    plt.xlabel("Permutation Importance")
    plt.title("Weather & Asset Features Driving Outages")
    plt.tight_layout()
    plt.savefig("chapter6_feature_importance.png")
    plt.show()

if __name__ == "__main__":
    df_outage = generate_storm_outage_data()
    train_outage_model(df_outage)