Customer Analytics and Demand Response

The Business Problem: Engaging Customers to Manage Demand

Electric grids must be built to meet peak demand even though those peaks occur only a few times each year. This leads to expensive infrastructure that sits underutilized for most hours. Demand response programs aim to address this by encouraging customers to shift or reduce consumption during critical periods, flattening peaks and easing stress on the grid.

However, not all customers respond the same way. Some households have highly flexible loads they can shift easily, while others cannot reduce usage without disruption. Programs that treat all customers alike often achieve disappointing results because incentives and messaging fail to match customer behavior.

Utilities need ways to identify which customers are best suited for demand response and to design tailored programs that maximize participation. Without this insight, they risk low enrollment, poor event compliance, and limited grid impact, undermining the value of demand-side resources.

The Analytics Solution: Using Data to Target and Segment Customers

Customer analytics uses data from advanced metering infrastructure, billing systems, and program participation records to understand consumption patterns and identify load flexibility. Smart meters provide granular consumption data that can reveal daily, weekly, and seasonal usage profiles for each household.

Clustering techniques can segment customers into groups based on similar load shapes. For example, some customers may show high evening peaks tied to cooking and HVAC usage, while others have flatter profiles or midday spikes associated with daytime occupancy. These segments inform which customers are most likely to reduce load in response to incentives or pricing signals.

Classification models can also predict participation likelihood, drawing on historical demand response enrollment and demographic data. This helps utilities prioritize outreach to those most likely to engage while avoiding costly campaigns aimed at customers unlikely to respond.

By combining behavioral segmentation with predictive modeling, utilities can refine demand response strategies, increase event performance, and avoid overbuilding supply-side resources.

Business Impact

Targeted demand response programs reduce peak load, defer costly infrastructure upgrades, and lower wholesale energy procurement during high-price periods. They also support integration of renewables by shifting load to times of abundant solar or wind generation.

Customers benefit from lower bills through participation incentives or time-based rates that reward off-peak consumption. Effective segmentation ensures these programs feel relevant and fair, avoiding customer dissatisfaction or program fatigue.

In competitive markets, successful demand response strategies can also provide revenue streams by aggregating flexible load into virtual power plants that bid into wholesale markets. Analytics makes this aggregation more precise and dependable.

Transition to the Demo

In this chapter’s demo, we will work with synthetic smart meter data to:

• Cluster customers into segments based on their daily load profiles.
• Visualize representative profiles for each segment to illustrate behavioral differences.
• Discuss how these segments inform targeted demand response design and incentive structures.

By the end, we will see how simple clustering techniques turn raw consumption data into actionable customer insights, making demand response programs more effective and efficient.

Code

"""
Chapter 9: Customer Analytics and Demand Response
Load segmentation using smart meter data and clustering for DR targeting.
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from tslearn.clustering import TimeSeriesKMeans

def generate_smart_meter_data(customers=200, days=14):
    """
    Generate synthetic smart meter data (hourly kWh for multiple customers).
    """
    rng = pd.date_range("2022-01-01", periods=24 * days, freq="H")
    profiles = []
    for _ in range(customers):
        base = np.random.uniform(0.3, 1.0)  # Base consumption
        morning_peak = base + 0.5 * np.exp(-0.5 * (np.arange(24) - 7) ** 2)
        evening_peak = base + 0.8 * np.exp(-0.5 * (np.arange(24) - 19) ** 2)
        daily_profile = (morning_peak + evening_peak) / 2
        load = np.tile(daily_profile, days) + np.random.normal(0, 0.05, 24 * days)
        profiles.append(load)
    df = pd.DataFrame(profiles, columns=rng)
    return df

def cluster_load_profiles(df):
    """
    Cluster customer load profiles using KMeans on daily averages.
    """
    scaler = StandardScaler()
    daily_avg = df.values.reshape(df.shape[0], -1, 24).mean(axis=1)  # Average daily profile
    daily_scaled = scaler.fit_transform(daily_avg)

    kmeans = KMeans(n_clusters=3, random_state=42)
    labels = kmeans.fit_predict(daily_scaled)

    plt.figure(figsize=(10, 6))
    for c in range(3):
        cluster_profiles = daily_avg[labels == c]
        plt.plot(cluster_profiles.T, color="gray", alpha=0.2)
        plt.plot(cluster_profiles.mean(axis=0), label=f"Cluster {c+1}", linewidth=2)
    plt.xlabel("Hour of Day")
    plt.ylabel("Average Load (kWh)")
    plt.title("Customer Segmentation for Demand Response")
    plt.legend()
    plt.tight_layout()
    plt.savefig("chapter9_dr_clusters.png")
    plt.show()

    return labels

def identify_dr_targets(df, labels, target_cluster=2):
    """
    Identify customers in the highest-load cluster for DR targeting.
    """
    high_load_customers = np.where(labels == target_cluster)[0]
    print(f"Identified {len(high_load_customers)} customers for DR targeting.")
    return high_load_customers

if __name__ == "__main__":
    df_smart = generate_smart_meter_data()
    labels = cluster_load_profiles(df_smart)
    dr_targets = identify_dr_targets(df_smart, labels)