Energy Management and Cost Reduction: A Predictive Approach to Monthly Electricity Billing in Mosul Residential Settings

Efficient electrical power management is crucial for both suppliers and consumers, significantly affecting consumer budgets and national economies. In Iraq, residential electricity consumption constitutes 56% of the national demand, leaving households with unpredictable monthly bills due to the absence of real-time usage monitoring. This financial uncertainty worsens grid instability and hinders efficient energy management. This paper proposes a methodology for energy management by estimating monthly electricity bills. We propose a predictive billing system that estimates monthly costs by analyzing kWh meter readings at 6-hour intervals, aligned with Iraq's tiered pricing structure (10–120 IQD/kWh). The readings were collected over 30 days from a household in Mosul (House-1). To simulate high-consumption households (House-2 and House-3), the dataset was subsequently expanded by generating statistically and mathematically modeled synthetic data based on House-1 readings. Five management strategies were tested, including dynamic reductions (2–10 kWh) triggered by tiered thresholds. This methodology not only provides an estimate but also offers suggestions to help reduce the total bill, ensuring it stays within a reasonable range. The implementation of the proposed methodology led to a decrease in electricity consumption between 6% and 23%, resulting in cost savings of 14% to 49% across three different household profiles. The tiered feedback approach (Scenario 4) achieved the highest monthly cost savings, with a 49% reduction for households consuming more than 4000 kWh per month. This approach empowers consumers to align usage with budgetary goals while alleviating grid stress. Future work will integrate IoT-based outage detection and mobile app deployment.