Case Studies and Integrated Pipelines

The Business Problem: Bringing Analytics Together for Systemwide Impact

Individual machine learning models for load forecasting, maintenance, or outage prediction are valuable, but their true potential emerges when integrated into broader workflows. Utilities operate complex systems where decisions in one domain often affect another. Maintenance planning influences outage risk, which in turn affects reliability metrics. Forecasting errors ripple into market operations and grid balancing.

Running these models in isolation creates silos, where insights are not connected or actionable at scale. For example, a predictive maintenance model may flag a transformer for attention, but without integrating that output into outage risk models or capital planning workflows, its value is diminished. Likewise, forecasts that remain in spreadsheets rarely inform operational systems in real time.

Utilities need end-to-end pipelines that chain these models together, orchestrating analytics in ways that align with operational processes. This requires not only running multiple models but also managing dependencies between them and delivering results directly to the systems and teams that act on them.

The Analytics Solution: Orchestrated Analytics for Utility Operations

Integrated pipelines bring multiple machine learning use cases under a single operational framework. Using orchestration tools, utilities can schedule models, manage their data dependencies, and chain outputs into downstream processes automatically.

For example, an outage risk pipeline might combine feeder-level weather exposure data, predictive maintenance scores for transformers, and vegetation risk models to produce a single prioritized list of circuits for storm preparation. Similarly, load forecasts can feed into both market bidding and distribution voltage optimization models, ensuring consistent inputs across operational domains.

These orchestrated pipelines reduce manual effort, enforce repeatability, and ensure that analytics results are available where and when they are needed. They also provide audit trails and monitoring necessary for regulated environments.

Operational Benefits

Bringing models together into unified workflows drives measurable benefits. Reliability improves when maintenance, vegetation, and weather models inform outage response as a coordinated system. Operational efficiency grows as redundant data preparation steps are eliminated. Analysts and engineers spend less time moving files between tools and more time interpreting results.

Moreover, integrated pipelines provide a pathway toward continuous improvement. As models are retrained or refined, updated outputs flow seamlessly into dependent processes, keeping the entire ecosystem current without manual intervention.

Transition to the Demo

In this chapter’s demo, we will build an integrated pipeline that:

Combines predictive maintenance, outage prediction, and load forecasting models into a single orchestrated workflow.
Automates data preparation and model execution steps.
Produces unified outputs suitable for dashboards or operational handoffs.

This exercise illustrates how utilities can move beyond isolated pilots and create connected analytics ecosystems that deliver consistent, actionable intelligence across their operations.

pyfile shortcode: missing param 'file'. Example: {{< pyfile file="script.py" >}}

Code

"""
Chapter 14: Case Studies and Implementation Roadmaps
End-to-end multi-use case pipelines integrating predictive maintenance, load forecasting, and outage prediction.
"""

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import classification_report
from statsmodels.tsa.arima.model import ARIMA
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

# --- Predictive Maintenance ---
def generate_asset_data(samples=500):
    np.random.seed(42)
    temp = np.random.normal(60, 5, samples)
    vibration = np.random.normal(0.2, 0.05, samples)
    oil_quality = np.random.normal(70, 10, samples)
    age = np.random.randint(1, 30, samples)
    failure_prob = 1 / (1 + np.exp(-(0.05*(temp-65) + 8*(vibration-0.25))))
    failure = np.random.binomial(1, failure_prob)
    return pd.DataFrame({"Temperature": temp, "Vibration": vibration, "OilQuality": oil_quality, "Age": age, "Failure": failure})

def predictive_maintenance_pipeline():
    df = generate_asset_data()
    X = df[["Temperature", "Vibration", "OilQuality", "Age"]]
    y = df["Failure"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)
    preds = model.predict(X_test)
    print("Predictive Maintenance Report:")
    print(classification_report(y_test, preds))

# --- Load Forecasting ---
def generate_load_data():
    date_rng = pd.date_range(start="2023-01-01", periods=24*30, freq="H")
    load = 900 + 100 * np.sin(2 * np.pi * date_rng.hour / 24) + np.random.normal(0, 30, len(date_rng))
    return pd.DataFrame({"timestamp": date_rng, "Load_MW": load})

def load_forecasting_pipeline():
    df = generate_load_data()
    ts = df.set_index("timestamp")["Load_MW"]
    model = ARIMA(ts, order=(2, 1, 2))
    fit = model.fit()
    forecast = fit.forecast(steps=24)
    plt.figure(figsize=(10, 4))
    plt.plot(ts[-72:], label="Observed", color="gray")
    plt.plot(forecast.index, forecast, label="Forecast", color="black")
    plt.legend()
    plt.title("Load Forecast (ARIMA)")
    plt.tight_layout()
    plt.savefig("chapter14_load_forecast.png")
    plt.show()

# --- Outage Prediction ---
def generate_outage_data(samples=1000):
    wind = np.random.normal(20, 7, samples)
    trees = np.random.uniform(0, 1, samples)
    rainfall = np.random.normal(50, 15, samples)
    outage_prob = 1 / (1 + np.exp(-(0.15*(wind-25) + 2*(trees-0.5))))
    outage = np.random.binomial(1, outage_prob)
    return pd.DataFrame({"WindSpeed": wind, "TreeDensity": trees, "Rainfall": rainfall, "Outage": outage})

def outage_prediction_pipeline():
    df = generate_outage_data()
    X = df[["WindSpeed", "TreeDensity", "Rainfall"]]
    y = df["Outage"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = GradientBoostingClassifier()
    model.fit(X_train, y_train)
    preds = model.predict(X_test)
    print("Outage Prediction Report:")
    print(classification_report(y_test, preds))

# --- Integrated Pipeline Execution ---
if __name__ == "__main__":
    print("\n--- Predictive Maintenance ---")
    predictive_maintenance_pipeline()
    print("\n--- Load Forecasting ---")
    load_forecasting_pipeline()
    print("\n--- Outage Prediction ---")
    outage_prediction_pipeline()