Normalize Energy Usage Data from CSV

You are given a complex CSV file representing energy usage data, where each row contains the following fields: timestamp, location, energy_usage_kwh, and energy_type. Your task is to parse this CSV data and normalize the energy_usage_kwh by converting it to megawatt-hours (MWh) and rounding to two decimal places. Additionally, filter out entries with negative energy usage values.

Input

• A string csv_data representing the CSV content.

Output

• A list of dictionaries, where each dictionary contains the normalized energy usage data with keys timestamp, location, and energy_usage_mwh.

Examples

Example 1:

Input: "timestamp,location,energy_usage_kwh,energy_type
2023-01-01T00:00:00Z,LocationA,1500,solar
2023-01-01T01:00:00Z,LocationB,-500,wind"  
Output: [{'timestamp': '2023-01-01T00:00:00Z', 'location': 'LocationA', 'energy_usage_mwh': 1.5}]

Example 2:

Input: "timestamp,location,energy_usage_kwh,energy_type
2023-01-01T00:00:00Z,LocationC,3000,solar"  
Output: [{'timestamp': '2023-01-01T00:00:00Z', 'location': 'LocationC', 'energy_usage_mwh': 3.0}]

Constraints

• The CSV will have at least one valid row.
• The energy_usage_kwh values are integers, which can be negative or positive.