90% Remote: Data Analyst with Energy Market KnowHow and Python Knowledge
Job description:
For our customers in the energy industry, we are currently looking for a Data Analyst (m/w/d) with Energy Market KnowHow and Python knowledge. Duration: 01.11.2024 - 30.06.2025 Capacity: 60% (3 days / week) Location: 90% Remote | max 10% on site in Chemnitz Project objective: The main objective of the project is to reduce adaptation energy by improving prediction accuracy and optimizing processes in handling RLM customers (registered performance measurement) using Business Strom Optima & BSE. This includes the more precise prediction of energy consumption and feed as well as the efficient use of customer information. An essential part of the project is the identification and elimination of vulnerabilities in existing forecasting methods and processes. Background: The customer strives to optimize their sales processes for RLM customers and increase the accuracy of their energy forecasts. The current supply process is fragmented and the structure of the delivery points requires a strategic realignment. The aim of the project is to minimise adaptation energy and establish data-based supply processes to better meet market requirements. Tasks: 1\. Comparison of IST rates for forecasting and delta analysis - Explanation: Analysing the deviations between IST rates and forecasts in the group of RLM customers to assess the accuracy of forecasting methods and identify vulnerabilities. This includes the recognition of specific patterns and causes for delta, such as unforeseen consumption collapses or inaccurate forecasting assumptions, for example for feeders. The identification and analysis of causes for deviations is the necessary data base to enable targeted adaptations of the forecast models. In the long term, deviations are better predicted and avoided. Two. feasibility analysis and model conception - Explanation: The current methods for calculating and forecasting the feeder data are to be evaluated. Concrete solutions are to be developed which lead to the integration of weather data. The accuracy of day forecasts is to be improved, which leads to a more accurate prediction of consumption and feed. A feasibility analysis should clarify whether and how weather data from weather stations can be integrated into day forecasts. Three. Assessment of customers in pre-contracting and information procurement - Explanation: Methods are to be developed to effectively assess customers in advance of contracting. Information on possible power supply or production should be collected in advance. For this purpose, ideas should be collected and checked for their feasibility. Various information channels are evaluated. The aim is to accurately collect relevant customer information in order to improve pre-contract assessments and to increase forecast accuracy. A comprehensive customer review and efficient use of information channels contribute to improving predictive accuracy. This reduces the need for adaptation energy, as more accurate and reliable forecasts can be created. Results and Deliverables: - Delta analysis report between IST rates and forecasts. - Recommendations for identifying feeders and improving predictive accuracy. - feasibility study for integrating weather data into day forecasts and their impact on adaptation energy. - Optimization proposals for the methods for assessing customers in the pre-contract and overview of effective information channels. Skills: - Energy market know-how: deep understanding of the requirements and challenges in the energy market, in particular for RLM customers in terms of load and their analysis. - Data Science and Forecast Modeling: Experience in the development and processing of replacement value profiles and forecast modelling using relevant data sources such as weather stations and market master data. - Python programming and automation: competences in the implementation and evaluation of time series with Python, including the use of libraries such as Pandas, scikit-learn and Prophet.