Expert Power Forecasting Short Term
Hi, my name is Melissa and I work at NextEra Energy as a “Short Term Expert Forecasting”
Disclaimer: Any reference to companies and people comes from the author’s fantasy.
About Melissa
Hi, my name is Melissa and I work at NextEra Energy as a “Short Term Expert Forecasting”
I have a degree in Mathematics from the University of Pisa, with a specialization in Statistics. After graduating, I started working as an electrical market analyst for a trading company. In this role, I gained experience in electricity supply and demand forecasting, risk management, and trading strategies.
In 2021, I started working at NextERA Energy, where I have the opportunity to apply my skills to the Italian and international electricity market. I develop electricity supply and demand forecasting models, which are used to support trading and investment decisions.
I am passionate about energy and the challenge of decarbonizing the sector. I believe that digital technologies have a fundamental role in this process, and I am convinced that statistical modelling can be a powerful tool to improve the efficiency and sustainability of the electricity market.
My skills and knowledge include:
- Master’s Degree in Mathematics with a focus on statistics
- At least 3 years’ work experience in short-term roles related to the electricity market, with extensive experience also on operations in foreign markets
- Proven modelling experience also concerning the dynamics of the European electricity market (algorithmic bidding strategies, competitor analysis, etc.)
- Good knowledge of python, r, matlab programming languages
- Good knowledge of Microsoft Office package (Word, Excel, Power Point, Access)
I am a proactive and collaborative person, with a strong interest in innovation. I am always looking for new challenges and opportunities to develop my skills.
Analysis of deviations
Analysis of deviations to assess the company’s economic, equity and financial performance concerning the targets.
I can guide you through the variance analysis process to assess the economic, equity and financial performance of a company compared to the set targets. This type of analysis is crucial for identifying any deviations from expectations and making corrective or strategic decisions. Below, you’ll find a step-by-step guide:
- Definition of Objectives and Targets
— Make sure you’re clear about the goals and targets set for the company. These can include financial metrics such as revenue, profit, cash flow, and industry-specific key performance indicators (KPIs). - Data Collection
— Get up-to-date financial data, such as financial statements, accounting reports, and other relevant information. Make sure that the data is comparable with the targets and that it is available for the reporting period. - Calculating Variances
— Calculate the differences between current performance and target targets for each metric. You can use formulas such as ((Current Data — Target) / Target) * 100% to get variance percentages. - Root Cause Analysis
— Investigate the causes of identified deviations. The causes can be internal (internal management, operational processes) or external (market conditions, regulatory changes). Root cause analysis is essential for making informed decisions. - Reflections on the Context
— Consider the business and market context. Some deviations could be the result of factors beyond the company’s control. Assess whether the deviations are due to exceptional circumstances or whether they are indicative of persistent trends. - Communication of Results
— Clearly communicate the results of the analysis to stakeholders and decision-makers. Presents the data clearly and provides explanations for corrective actions if necessary. - Corrective Action
— If necessary, develop and implement corrective action plans. These plans should specifically address the causes of deviations and aim to bring performance back on the desired trajectory. - Continuous monitoring
— Implement continuous monitoring systems to follow performance trends over time. Regularly compare results to targets and make adjustments to strategies if necessary. - Documentation
— Document the variance analysis process, the actions taken, and the results obtained. This will be useful for future reference and to improve the process over time.
Remember that variance analysis is a dynamic and continuous process that requires constant updating in response to changing market and business conditions.
Group reporting
Support the team in the preparation of group reporting
As an “Expert Forecasting Short Term” at NextERA Energy, my experience and training allow me to provide significant support to my team in the preparation of group reporting. Here are some of the tasks I could do to ensure complete and accurate reporting:
- Market Data Analysis
— I collect and analyze data related to the short-term electricity market and, where appropriate, also in foreign markets. I monitor price dynamics, resource availability, and other relevant factors to understand the market environment. - Modelling and Projections
— I use my modelling skills to create accurate forecasts regarding the key parameters of the electricity market. These projections may include price forecasts, trading volumes, and other relevant variables. - Elaboration of Customized Reports
— I contribute to the creation of customized reports that respond to the specific needs of the group. These reports include operational performance data, analysis of deviations from targets, and other key performance indicators. - Analysis of Algorithmic Bidding Strategies
— I deepen the analysis of algorithmic bidding strategies in the context of the internal market. This can include optimizing bidding parameters to maximize results. - Competitor Analysis
— I carry out in-depth analyses of competitors in the electricity market, identifying their strategies, performance and possible impacts on our company’s activities. - Data & Systems Integration
— I work with the team to integrate data from different sources and systems. I ensure that the data is consistent and reliable to ensure consistency in group reporting. - Strategic Planning Support
— I provide strategic insight based on the analysis of market data and observed trends. This supports the team’s strategic planning and contributes to informed decisions. - Training and Refresher Courses
— I keep the team informed about the latest market trends, new developments in the industry and advanced modelling methodologies. This helps to keep the team up-to-date and knowledgeable. - Clear and Effective Communication
— I communicate the results of the analyses clearly and understandably, ensuring that the team can draw meaningful conclusions from the group reporting.
Essentially, my role is to provide analytical and strategic support to the team, leveraging my expertise in the electricity market and quantitative skills to contribute to the overall success of the company.
Operational, Economic and Financial Data
Ensure the timely preparation of datasets on operational, economic and financial data.
To ensure the timely preparation of operational, economic and financial data datasets at NextERA Energy, I adopt a set of practices and activities that aim to maximize the efficiency and accuracy of the process. Here are some actions I could take:
- Process Automation
— I implement automation solutions for the collection, processing and preparation of datasets. Automation reduces the risk of human error and speeds up the overall process. - Using Business Intelligence (BI) Tools
— Leverage advanced BI tools to extract data from different sources, create interactive dashboards, and generate reports quickly and effectively. These tools make it easy to analyze data and create datasets. - Collaboration with the Departments Involved
— I work closely with the various departments of the company to understand their data needs and ensure that I gather all the necessary information on time. - Schedule and Define Calendar
— Establish a detailed timetable for data collection and processing, taking into account the deadlines for the preparation of periodic reports. This helps ensure on-time preparation of datasets. - Continuous Monitoring of Data Sources
— Implement continuous monitoring systems of data sources to detect any anomalies or delays. This helps to intervene promptly in case of problems. - Validation and Quality Control
— I apply data validation procedures and quality controls to ensure the integrity and accuracy of the information. This minimizes errors that could delay the process. - Scalability
— I structure processes to be scalable, allowing you to handle large volumes of data without compromising timeliness. This is especially important in environments where the amount of data can vary considerably. - Staff Training
— I offer training to the staff involved in data management, ensuring that they are competent in the use of the tools and in the correct execution of the procedures. - Implementation of Standard Procedures
— Establish standardized procedures for data collection and processing, reducing complexity and ensuring greater consistency and timeliness in the process. - Rapid Response to Urgent Needs
— I am prepared to respond quickly to urgent requests, for example during critical financial situations or periods of intensive reporting.
The combination of automation, cross-functional collaboration, continuous monitoring, and well-structured procedures helps ensure timely provisioning of operational, economic, and financial data datasets.
Models and Processes Related to Reporting and Consolidation Activities
To contribute to the evolution of the models and processes related to reporting and consolidation activities at NextERA Energy, I adopt a strategic and innovative approach. Here are some key actions I could take:
- Research and Implementation of New Technologies
— I constantly monitor new technologies in the field of business intelligence, data analysis and modelling. I introduce innovative solutions to improve the efficiency and accuracy of reporting processes. - Analysis of the Best Practices of the Sector
— I study the best practices of the sector in terms of financial modeling, consolidation and reporting. I introduce advanced methods based on recognized standards to improve data quality and process effectiveness. - Cross-Functional Collaboration
— I actively collaborate with cross-functional teams, including finance, IT and business analyst teams. This collaboration helps to fully understand the needs of each department and develop integrated solutions. - Development and Implementation of Predictive Models
— I apply predictive models to improve financial forecasts and to anticipate any deviations. Using advanced templates can contribute to more proactive and informed management. - Process Optimization
— Carefully review existing reporting and consolidation processes to identify opportunities for optimization. I introduce more efficient methodologies and minimize the time it takes to prepare reports. - Continuous Updating of Models
— I keep financial models up-to-date in response to changes in the market, the company or regulations. This ensures that the reports accurately reflect the current situation. - Focus on Sustainability
— Integrate sustainability elements into reporting models, responding to the growing focus on environmental, social and governance (ESG) issues. This may include sustainable performance indicators and related communication to stakeholders. - Implementation of Interactive Dashboards
— I create interactive dashboards that allow users to explore data in more detail. This makes it easier to understand trends and make informed decisions. - Participation in Training and Seminars
— I participate in trainings and seminars to stay up-to-date on new trends in the field of financial and consolidated reporting. I apply the new knowledge acquired to improve internal processes. - Continuous Feedback and Adaptation
— I collect constant feedback from team members and report from end users. I use this information to make continuous adjustments and improvements to models and processes.
Contributing to the evolution of models and processes requires a flexible approach, oriented towards innovation and the continuous search for improvements. The combination of advanced technologies, cross-functional collaboration, and a focus on best practices allows you to drive change and ensure growth and sustainability over time.
Short-Term Consumption Forecast for the Customer Portfolio
As an “Expert Forecasting Short Term” at NextERA Energy, my role includes supporting the definition of the best consumption forecast over the short-term horizon for the client portfolio. To ensure accurate and timely forecasting, I adopt several strategies:
- Historical Data Analysis
— I carefully review customers’ historical consumption data to identify patterns, seasonality, and trends. In-depth analysis of past data provides a solid foundation for future forecasting. - Statistical Modelling
— I use advanced statistical models, such as ARIMA (AutoRegressive Integrated Moving Average) models or regression models, to model past consumption behaviour and predict future trends. - Integration of External Data
— I incorporate relevant external data that may affect consumption, such as weather data, holidays, or special events. This integration helps improve the accuracy of forecasts by considering external variables. - Machine Learning and Predictive Algorithms
— I implement machine learning algorithms, such as neural networks or machine learning algorithms, to address the complexity and non-linearity of consumer data. These algorithms can dynamically adapt to changes in consumption patterns. - Continuous Feedback and Updating of Models
— I constantly monitor the performance of forecasting models and apply updates in response to changes in customer behaviours or market context. This ensures the relevance and accuracy of forecasts over time. - Customer Segmentation
— I divide the customer portfolio into homogeneous segments based on consumption behaviour. This allows forecasts to be tailored more precisely, taking into account the specific characteristics of each group. - Collaboration with Technical and Operational Teams
— I work closely with the technical and operational teams to understand the dynamics of the electricity market, operating restrictions and any changes in supply conditions. This collaboration helps integrate actionable information into forecasts. - Evaluation of Economic and Market Indicators
— I evaluate economic and market indicators that could affect energy consumption, such as economic growth rates, energy prices, and regulatory policies. This analysis contributes to a more informed forecast. - A/B Testing and Cross-Validation
— I perform A/B testing and cross-validation to evaluate the robustness of forecasting models. These tests help identify and correct any weaknesses in the methodologies used. - Clear Communication of Expected Fluctuations
— Communicate to the team and stakeholders the expected fluctuations in consumption, highlighting the main drivers and providing clear explanations in case of significant deviations from forecasts.
The goal is to develop consumption forecasts that are accurate, up-to-date and able to respond dynamically to changes in the context. The combination of advanced modelling, cross-functional collaboration and a data-driven approach helps to improve the quality of forecasts in the short term.
Production and Consumption Forecasting Process
To implement the UP (Production Unit) and UC (Consumption Unit) forecasting process and operational oversight on the national and foreign electricity market, as an “Expert Forecasting Short Term” at NextERA Energy, I adopt a series of key steps and strategies. Here’s how you can proceed:
- Analysis of the Market Context
— I would carry out an in-depth analysis of the market context both nationally and abroad. This would include understanding supply and demand dynamics, weather conditions, energy policies and other factors that can affect the electricity market. - Data and Information Collection
— I would collect historical and current data relevant to the electricity market, including data on consumption, production, prices and other key indicators. I would integrate data from reliable and up-to-date sources. - Statistical Modeling and Predictive Algorithms
— I would use advanced statistical models, such as ARIMA models or machine learning algorithms, to predict Full Usage and Coverage Usage. These models should be able to dynamically adapt to market changes. - Market Segmentation
— I would divide the market into homogeneous segments to refine the forecasts. This segmentation could be based on criteria such as consumer type, geographic region, or other factors that influence market dynamics. - Analysis of Historical Trends
— I would analyze historical trends in Full Utilization and Coverage Utilization to identify recurring patterns and seasonality. This analysis would contribute to a better understanding of the variables that drive market behaviour. - Integration of Internal and External Data
— I would integrate internal company data with external information, such as weather data, economic forecasts and industry regulations. This integration would enrich the forecast with external influential variables. - Collaboration with Market Operators
— I would collaborate with market participants, institutions and other stakeholders to obtain updated information on market conditions, new regulations and other relevant variables. - Continuous Model Update
— I would maintain a continuous procedure of updating the models in response to changes in the market context, in the trend of key variables and the behaviour of operators. - Operational Unit
— I would implement an operational monitoring system to monitor market conditions in real-time and intervene promptly in the event of significant deviations from forecasts. This could include defining alerts and key performance indicators (KPIs). - Clear and Quick Communication
— I would clearly and quickly communicate forecasts, relevant changes and corrective actions taken to the operations team and decision-makers.
The objective is to create a dynamic, precise and adaptable forecasting process that supports operational monitoring to ensure a timely response to changes in the electricity market. The combination of advanced modelling, collaboration with key stakeholders and effective operational oversight will contribute to the optimal management of PU and UC activities in the electricity market.
Analyze performance, monitor markets, and market participation strategies
To analyze the performance of the actions taken, monitor the markets, the behaviour of competitors, and the weather scenario, and dynamically adapt forecasting models and market participation strategies, I adopt a methodical and proactive approach. Here’s how I would behave:
- Continuous Market Monitoring
— I implement continuous monitoring systems to collect real-time data from electricity markets, analyzing price movements, demand and supply, and other relevant indicators. The use of APIs and advanced data analysis tools supports this activity. - Competitor Behavior Analysis
— I constantly monitor the actions and strategies of competitors in the electricity market, trying to identify any changes in their bidding strategies, market positions and new developments. This analysis provides valuable insights to adapt your strategies. - Analysis of Meteorological Conditions
— I integrate meteorological data into analyses to understand how climate conditions influence energy supply and demand. This information is particularly relevant in industries where weather conditions have a significant impact on energy production and consumption. - Key Performance Indicators (KPIs)
— I define and carefully monitor the Key Performance Indicators (KPIs) that reflect the effectiveness of market participation strategies. These KPIs may include financial performance, market share gained, forecast accuracy and other relevant metrics. - Analysis of Deviations Compared to Forecasting Models
— I constantly analyze deviations between the forecasts made and the actual results. I identify the causes of deviations, evaluating whether they are due to changes in market variables, errors in forecasting models or other factors. - Continuous Feedback from the Operational Unit
— I get regular feedback from the operational team and the teams involved in the execution of market participation strategies. This direct feedback contributes to a practical understanding of the operational dynamics and any necessary adjustments. - Continuous Updating of Forecasting Models
— In response to deviation analyses and changes in market conditions, I continuously update forecasting models. This may involve optimizing model parameters or adopting new approaches based on machine learning. - Adaptation of Strategies
— Based on the analyses carried out, I dynamically adapt market participation strategies. This may include reviewing pricing strategies, adjusting bid quantities, or changing bidding tactics. - Rapid Response to Extraordinary Events
— I am promptly responsive to extraordinary or unexpected events that could influence the electricity market. This flexibility allows you to quickly adapt strategies in situations of sudden change. - Clear and Rapid Communication of Actions Taken
— I communicate to the team and stakeholders the actions taken in response to the analyses carried out, explaining the reasons behind the changes to the strategies and the expected results.
The approach is to constantly integrate learning from data analysis, operational feedback and market dynamics to continuously optimize market participation strategies and forecasting models. Flexibility and readiness to respond to changes in the context are key elements of this process.
Definition of Statistical-Quantitative Models
To support the definition of statistical-quantitative models for activities in the short-term power portfolio & flexibility field, in collaboration with the other functions in the energy management field, I would adopt a series of actions and strategies. Here’s how I could contribute to this initiative:
- Cross-functional collaboration
— I would establish close collaboration with other functions within the energy management team, including trading experts, financial analysts, energy engineers and other professionals involved. This collaboration facilitates the exchange of knowledge and skills, allowing a holistic and integrated vision of the activities. - Analysis of Needs and Objectives
— I would work in tandem with the other functions to fully understand the specific needs and objectives related to the short-term power portfolio & flexibility. This needs analysis to help define the key parameters to consider in quantitative statistical models. - Data Collection and Preparation
— I would collaborate with data specialists to collect and prepare the data needed for statistical analysis. This could include historical consumption data, weather data, energy prices and other relevant variables. - Definition of Key Performance Indicators (KPIs)
— In collaboration with stakeholders, I would define key performance indicators (KPIs) that reflect the specific objectives of the short-term power portfolio & flexibility. These KPIs become key parameters for evaluating model performance. - Development of Statistical and Quantitative Models
— I would use my experience in statistical and quantitative modelling to develop models that meet specific needs. These models can include demand forecasting, risk analysis, portfolio optimization, and other decision-making tools. - Evaluation of Flexibility Options
— I would analyze and quantify the flexibility options available in the energy portfolio, such as distributed energy resource (DER) management, flexibility in supply contracts and other operational strategies. - Model Testing and Validation
— Conduct in-depth testing and validation of developed models using historical data and simulated scenarios. This ensures that the models are robust and reliable in different operating conditions. - Power Portfolio Optimization
— Collaborate with trading analysts and other experts to optimize the power portfolio based on forecasts, market conditions and flexibility objectives. This may include bidding strategies, asset management and other trading activities. - Implementation of Machine Learning Techniques if Necessary
— If appropriate, I would implement machine learning techniques to address complexity and non-linearity in the data. These techniques can improve the accuracy of predictions and the adaptive capacity of models. - Effective Communication
— I effectively communicate the results of the analyses and models developed to stakeholders, ensuring a clear understanding of the operational and decision-making implications.
Inter-functional collaboration and an approach centred on the specific needs of energy management are fundamental for the success in the implementation of statistical-quantitative models in short-term power portfolio & flexibility activities. The goal is to develop solutions that contribute to optimal management of the energy portfolio, maximizing benefits and addressing operational challenges.
