Prediction of electricity consumption

In an era where sustainable energy management is paramount, predicting electricity consumption is more than just a numerical exercise; it’s a commitment to a greener future. When entrusted with the task of forecasting the future electricity needs for a city, I embarked on a data-driven journey that fused the power of advanced time series models with insightful analysis.

The challenge was clear: to provide the company with a reliable forecast of electricity consumption. To accomplish this, I turned to Time Series models, specifically ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal ARIMA). These models are renowned for their ability to unravel patterns and trends hidden within temporal data.

The journey began with data, vast volumes of historical electricity consumption records. I meticulously cleaned, organized, and prepared this data, laying the foundation for meaningful analysis. With a deep dive into the intricacies of ARIMA and SARIMA, I harnessed their potential to capture seasonality, trends, and anomalies in the consumption patterns.

The culmination of this effort was a forecast that stretched into the future, providing valuable insights into the city’s upcoming electricity needs. These insights weren’t just numbers on a spreadsheet; they were actionable intelligence that could shape energy procurement strategies and infrastructure planning.

To convey these findings effectively, I turned to PowerPoint. Through compelling slides, I elucidated the nuances of the predictions. The visuals complemented the data, making it accessible and understandable to both technical and non-technical stakeholders.

This project reflects my dedication to harnessing data for informed decision-making, especially in critical domains like energy management. It showcases my proficiency in advanced statistical modeling and data presentation, emphasizing how accurate predictions can pave the way for a more sustainable and efficient future.