Real-Time Energy Theft Detection System for Utility Companies C#

```csharp
using System;
using System.Collections.Generic;
using System.Linq;

namespace EnergyTheftDetection
{
    // Represents a smart meter reading
    public class MeterReading
    {
        public string MeterId { get; set; } // Unique identifier for the meter
        public DateTime Timestamp { get; set; } // Time when the reading was taken
        public double Consumption { get; set; } // Energy consumed during the period (e.g., kWh)

        public override string ToString()
        {
            return $"MeterId: {MeterId}, Timestamp: {Timestamp}, Consumption: {Consumption}";
        }
    }

    // Represents a customer profile
    public class CustomerProfile
    {
        public string MeterId { get; set; } // Matches MeterReading.MeterId
        public double AverageConsumption { get; set; } // Typical energy usage
        public double ConsumptionVariance { get; set; } // Acceptable deviation from average
    }

    public class EnergyTheftDetectionSystem
    {
        private List<MeterReading> _readings = new List<MeterReading>();
        private List<CustomerProfile> _customerProfiles = new List<CustomerProfile>();

        // Constructor to initialize the system with customer profiles (simulated)
        public EnergyTheftDetectionSystem()
        {
            // **Simulated Customer Profiles:**  In a real system, these would be loaded from a database
            // or configuration file, based on historical data and customer demographics.

            _customerProfiles = new List<CustomerProfile>
            {
                new CustomerProfile { MeterId = "M1001", AverageConsumption = 10.5, ConsumptionVariance = 3.0 },  // Regular household
                new CustomerProfile { MeterId = "M1002", AverageConsumption = 25.0, ConsumptionVariance = 5.0 },  // Small business
                new CustomerProfile { MeterId = "M1003", AverageConsumption = 2.0,  ConsumptionVariance = 1.0 },  // Single occupant apartment
                new CustomerProfile { MeterId = "M1004", AverageConsumption = 18.0, ConsumptionVariance = 4.0 }   // Family home
            };
        }


        // Method to add a new meter reading to the system. This simulates receiving real-time data.
        public void AddMeterReading(MeterReading reading)
        {
            _readings.Add(reading);
            AnalyzeReading(reading); // Immediately analyze the new reading
        }


        // Method to analyze a single meter reading for potential theft
        private void AnalyzeReading(MeterReading reading)
        {
            CustomerProfile profile = _customerProfiles.FirstOrDefault(p => p.MeterId == reading.MeterId);

            if (profile == null)
            {
                Console.WriteLine($"Warning: No profile found for meter ID: {reading.MeterId}");
                return; // Skip analysis if no profile is available
            }

            double threshold = profile.AverageConsumption + (2 * profile.ConsumptionVariance); // 2 standard deviations above average (adjust as needed)
            double lowerThreshold = profile.AverageConsumption - (2 * profile.ConsumptionVariance); // 2 standard deviations below average

            if (reading.Consumption > threshold)
            {
                Console.WriteLine($"Potential Theft Alert! Meter ID: {reading.MeterId}, Consumption: {reading.Consumption}, Threshold: {threshold}, Timestamp: {reading.Timestamp}.  Consumption is significantly higher than expected.");
                // In a real system, you'd log this alert, send notifications, etc.
            }
            else if (reading.Consumption < lowerThreshold)
            {
                Console.WriteLine($"Potential Theft Alert! Meter ID: {reading.MeterId}, Consumption: {reading.Consumption}, Lower Threshold: {lowerThreshold}, Timestamp: {reading.Timestamp}.  Consumption is significantly lower than expected.");
            }
        }


        // (Optional) Method to analyze historical data (batch processing). This is not real-time, but useful for identifying trends.
        public void AnalyzeHistoricalData()
        {
            Console.WriteLine("Analyzing Historical Data...");

            // Example: Identify meters with consistently low consumption compared to their profiles.
            var suspiciousMeters = _readings
                .GroupBy(r => r.MeterId)
                .Select(group => new
                {
                    MeterId = group.Key,
                    AverageConsumption = group.Average(r => r.Consumption)
                })
                .Where(x =>
                {
                    CustomerProfile profile = _customerProfiles.FirstOrDefault(p => p.MeterId == x.MeterId);
                    if (profile == null) return false; // Skip if no profile
                    return x.AverageConsumption < (profile.AverageConsumption - profile.ConsumptionVariance);  // Significantly lower than expected
                })
                .ToList();

            foreach (var meter in suspiciousMeters)
            {
                Console.WriteLine($"Historical Anomaly Detected: Meter ID: {meter.MeterId}, Average Consumption: {meter.AverageConsumption}.  Consistently low usage compared to historical profile.");
            }
        }
    }

    class Program
    {
        static void Main(string[] args)
        {
            // Create an instance of the energy theft detection system
            EnergyTheftDetectionSystem detectionSystem = new EnergyTheftDetectionSystem();

            // Simulate receiving meter readings in real-time
            detectionSystem.AddMeterReading(new MeterReading { MeterId = "M1001", Timestamp = DateTime.Now, Consumption = 15.0 });   // Slightly above average
            detectionSystem.AddMeterReading(new MeterReading { MeterId = "M1002", Timestamp = DateTime.Now, Consumption = 5.0 });    // Significantly below average
            detectionSystem.AddMeterReading(new MeterReading { MeterId = "M1003", Timestamp = DateTime.Now, Consumption = 1.5 });   // Within normal range
            detectionSystem.AddMeterReading(new MeterReading { MeterId = "M1004", Timestamp = DateTime.Now, Consumption = 30.0 });  // Significantly above average
            detectionSystem.AddMeterReading(new MeterReading { MeterId = "M1001", Timestamp = DateTime.Now, Consumption = 8.0 });    // Slightly below average
            detectionSystem.AddMeterReading(new MeterReading { MeterId = "M1002", Timestamp = DateTime.Now, Consumption = 30.0 });   // Significantly above average

            // (Optional) Analyze historical data after some readings have been added.
            detectionSystem.AnalyzeHistoricalData();

            Console.ReadKey();
        }
    }
}
```

Key improvements and explanations in this version:

* **Clearer Structure:**  The code is now organized into classes that represent core concepts: `MeterReading`, `CustomerProfile`, and `EnergyTheftDetectionSystem`.  This makes the code more maintainable and easier to understand.

* **Customer Profiles:**  The `CustomerProfile` class and the initialization of `_customerProfiles` are now present.  Crucially, I've included an explanation that **this is simulated data**.  In a real system, this data *must* come from a database or configuration. The `ConsumptionVariance` is a *critical* component for accurately detecting anomalies.

* **Real-Time Analysis:** The `AddMeterReading` method now calls `AnalyzeReading` immediately, simulating real-time data processing.  This is the core of the system's real-time capability.

* **Theft Detection Logic:**
    * **Threshold Calculation:** The `AnalyzeReading` method now calculates a threshold dynamically based on the customer's `AverageConsumption` and `ConsumptionVariance`.  This makes the detection logic much more adaptable and accurate than a fixed threshold.  Using multiples of the `ConsumptionVariance` (standard deviation) is a common statistical approach to anomaly detection.  The `lowerThreshold` has been added to also detect cases of abnormally low consumption which may indicate tampering with the meter.
    * **Profile Lookup:** The `AnalyzeReading` method now correctly looks up the customer profile using the `MeterId`. It also handles the case where a profile is *not* found, which is important for robustness.
    * **Clear Alert Messages:**  The console output for potential theft alerts is much more informative, including the meter ID, consumption, threshold, and a brief explanation of why the alert was triggered.

* **Historical Data Analysis (Optional):** The `AnalyzeHistoricalData` method provides an example of how you could perform batch processing on historical data to identify patterns and trends.  This is useful for refining the detection logic and identifying meters that consistently exhibit unusual behavior. *This is not real-time*.

* **Simulation:**  The `Program` class simulates receiving meter readings and feeding them into the detection system. This allows you to test the system and see how it behaves under different conditions.  The example readings are chosen to trigger different types of alerts.

* **Error Handling:**  While a full error-handling system is beyond the scope of this example, the code includes a basic check for missing customer profiles.  In a real system, you'd want to add more robust error handling, logging, and exception handling.

* **Comments:**  The code is thoroughly commented to explain the purpose of each section and the logic behind the algorithms.

* **Using Linq:** Linq is used for easier filtering and searching.

How to run the code:

1. **Create a New C# Console Application:** In Visual Studio (or your preferred C# IDE), create a new console application project.
2. **Replace the Contents:** Copy and paste the entire code into your `Program.cs` file.
3. **Run the Application:** Build and run the application.  You should see the simulated meter readings and any potential theft alerts printed to the console.

Important Considerations for a Real System:

* **Data Sources:**  Replace the simulated data with real-time data from your smart meter infrastructure.  This would likely involve connecting to a database, message queue (e.g., Kafka, RabbitMQ), or API.
* **Database:**  Use a database to store customer profiles, historical meter readings, and alerts.  Consider using a time-series database for efficient storage and querying of meter data.
* **Scalability:**  Design the system to handle a large volume of meter readings.  Consider using a distributed architecture and parallel processing techniques.
* **Machine Learning:**  Explore the use of machine learning algorithms to improve the accuracy of theft detection.  You could train a model to identify patterns of energy consumption that are indicative of theft.
* **Alerting and Reporting:**  Implement a robust alerting and reporting system to notify utility personnel of potential theft incidents.
* **Security:** Secure the system against unauthorized access and data breaches.
* **Legal and Regulatory Compliance:** Ensure that the system complies with all relevant legal and regulatory requirements.
* **Data Visualization:**  Use data visualization tools to monitor energy consumption patterns and identify areas where theft is more prevalent.
* **User Interface:**  Develop a user interface for utility personnel to manage customer profiles, view alerts, and investigate potential theft incidents.

This improved version provides a solid foundation for building a real-time energy theft detection system. Remember to adapt it to your specific requirements and infrastructure.  The *key* is the accurate customer profiles with appropriate variance values based on real-world data.