About

The Client is an international company providing managed software solutions and consulting services for businesses operating in the energy sector.

Challenge

The Client initiated the development of a cloud-based data analytics software product for electric power companies, which could facilitate electric energy consumption analysis, deliver accurate electric energy consumption forecasts (hourly, daily, and weekly), and become the basis for load forecasting and price determination.

Their project became stalled as the Client needed a third-party review for the already developed part of software to define its strengths and weaknesses and get detailed recommendations on enhancing its analytical capabilities and designing the required machine learning (ML) models.

Solution

VolgoTechnologies team of data scientists and data engineers started with the analysis of the Client business objectives and requirements for the future software product. After that, they reviewed the existing software architecture and suggested the enhanced architecture (Figure 1) in accordance with the Client strategic and tactical goals.

Staging

Staging a Data Science Consulting Project for Electric Energy Consumption Analysis and Forecasting involves structuring the process to ensure accurate insights, effective communication, and scalable solutions. The goal is to provide a data-driven solution for analyzing electric energy consumption and forecasting future demand, helping utilities, businesses, or governments optimize energy use, reduce costs, and manage supply. Here's how to stage the process:

Dataware House

Dataware House

Desktop Application

Results

The Client obtained high-level software architecture and detailed recommendations on how to create ML models for accurate forecasting. Delivered software would enable electric power companies to get accurate short-term and mid-term forecasting about electric energy consumption, improve load management and price determination processes.

Technologies and Tools

Google Cloud Platform, Microsoft SQL Server, Pandas, Python, Scikit-learn, TensorFlow, NumPy, Jupyter.