Course Introduction
Renewable energy projects generate increasingly diverse operational, commercial, environmental, and resource datasets that must be transformed into reliable intelligence for planning and performance management. This Renewable Energy Data Analytics and Performance Intelligence Training Course develops practical capabilities for analyzing solar, wind, hydropower, geothermal, biomass, and other renewable-energy datasets, with emphasis on capacity, generation, availability, utilization, resource variability, forecasting, operational performance, and decision support. IRENA’s current statistical resources demonstrate the breadth of renewable-energy information available across technologies, countries, capacity, generation, energy balances, costs, finance, and deployment.
The course establishes a structured approach to renewable energy data analytics, beginning with the identification, integration, validation, and interpretation of data from generation assets, SCADA systems, smart meters, weather stations, resource assessments, maintenance platforms, energy-management systems, market systems, and financial records. Participants examine how capacity and generation should be interpreted consistently, including the distinction between installed or connected capacity and actual electricity generation. IRENA reports capacity in MW and generation in GWh and provides renewable electricity statistics by country, technology, grid connection, data type, and year, creating a useful foundation for analytical work.
A central theme is renewable asset performance intelligence. Participants learn how to evaluate capacity factor, availability, generation efficiency, production losses, curtailment, downtime, resource variability, ramping behavior, and deviations between expected and actual production. Performance analytics can be applied at plant, turbine, inverter, generator, unit, site, portfolio, regional, and technology levels. EIA's renewable-energy modeling framework similarly incorporates available generating capacity, capacity factor, resource characteristics, and seasonal and hourly renewable-resource values when analyzing renewable electricity technologies and their interaction with storage and power-system dispatch.
The program also develops analytical methods for forecasting renewable generation and understanding the operational impact of variable resources. Participants explore time-series analysis, weather-sensitive forecasting, resource-to-power relationships, production baselines, forecast-error analysis, scenario modeling, and short-, medium-, and long-term performance assessment. EIA provides renewable electricity capacity and generation data alongside broader electricity datasets, while its renewable-energy modeling recognizes the importance of regional, seasonal, and hourly characteristics for intermittent renewable technologies.
Renewable energy performance intelligence also requires commercial and portfolio-level analysis. The course therefore connects technical performance indicators with revenue, operating cost, maintenance, investment, project development, grid integration, storage, and portfolio-management questions. Participants learn how to compare assets and technologies using consistent KPIs, investigate underperformance, identify recurring loss patterns, evaluate operational scenarios, and communicate analytical findings through dashboards and executive reports. IRENA's renewable-energy datasets include capacity, generation, energy balances, costs, finance, and investment information, supporting a broader analytical perspective beyond individual plant production.
The final component is an applied renewable energy analytics and performance intelligence project. Participants integrate generation, asset, weather, resource, maintenance, operational, and commercial datasets to construct performance indicators, identify production drivers and losses, analyze variability, develop forecasts, and create management dashboards. The approach is designed for organizations managing renewable assets, energy portfolios, grid-connected generation, distributed resources, or renewable-energy programs and emphasizes reproducible analysis, data-quality controls, actionable insights, and evidence-based performance improvement.
Duration
5 days
Who Should Attend
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Renewable energy managers responsible for generation performance, operational reporting, portfolio oversight, and data-driven asset management.
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Solar PV and solar thermal professionals seeking stronger analytical capabilities for production, resource, equipment, and performance assessment.
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Wind energy professionals involved in turbine performance, wind-resource analysis, generation forecasting, availability, and operational intelligence.
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Hydropower professionals analyzing generation, water-resource variability, plant utilization, operational efficiency, and production trends.
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Renewable energy data analysts, business intelligence professionals, and reporting specialists working with operational and performance datasets.
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Asset managers and operations professionals responsible for renewable power plant availability, production optimization, maintenance performance, and KPI management.
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Energy planners, system analysts, and grid professionals evaluating renewable generation profiles, variability, forecasting, and integration requirements.
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Renewable energy project developers, investors, and commercial professionals requiring quantitative insight into project performance, production, and portfolio economics.
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Operations and maintenance teams seeking data-driven methods for identifying downtime, recurring losses, equipment issues, and performance degradation.
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Energy consultants, researchers, engineers, and technical advisors working with renewable energy performance, resource, forecasting, or investment datasets.
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Government, regulatory, and energy-agency professionals analyzing renewable deployment, generation performance, technology trends, and energy-system indicators.
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Sustainability, energy-transition, and corporate energy professionals seeking practical analytics for renewable procurement, performance monitoring, and strategic decision-making.
Course Objectives
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Develop a comprehensive framework for renewable energy data analytics covering generation, capacity, resource conditions, asset performance, operational events, maintenance, commercial indicators, and portfolio intelligence.
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Integrate and prepare renewable energy datasets from SCADA, meters, weather systems, resource assessments, maintenance platforms, market systems, and project databases for reliable analytical use.
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Analyze renewable generation profiles across different technologies and time horizons while distinguishing installed capacity, available capacity, actual generation, utilization, and performance indicators.
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Calculate and interpret renewable energy performance metrics including capacity factor, availability, production variance, downtime, losses, utilization, forecast error, and technology-specific operational indicators.
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Apply statistical and time-series methods to identify production trends, seasonality, variability, anomalies, degradation patterns, and relationships between renewable resources and electricity generation.
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Develop renewable generation forecasts using historical production, weather, resource, calendar, operational, and other explanatory variables while evaluating forecast accuracy and uncertainty.
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Analyze asset-level and portfolio-level performance to identify underperforming facilities, recurring operational losses, equipment issues, curtailment patterns, and opportunities for performance improvement.
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Evaluate renewable energy scenarios involving solar, wind, hydro, storage, distributed generation, resource variability, grid conditions, capacity expansion, and changing operational requirements.
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Build renewable energy dashboards and performance reports that communicate operational KPIs, production trends, forecast results, asset comparisons, anomalies, and management insights clearly.
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Complete an integrated renewable energy analytics project that converts multi-source operational data into actionable performance intelligence for asset management, planning, investment, and decision support.
Comprehensive Course Outline
Module 1: Renewable Energy Data Analytics and Performance Intelligence Foundations
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Examine the renewable energy value chain across solar, wind, hydropower, geothermal, biomass, storage, distributed generation, grid integration, operations, and commercial management.
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Explore renewable energy data domains including capacity, generation, resource availability, weather, equipment status, maintenance, market activity, costs, and project performance.
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Define core renewable performance KPIs such as capacity factor, availability, utilization, production variance, downtime, curtailment, losses, and forecast accuracy.
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Establish a renewable energy performance intelligence framework connecting operational data with asset management, planning, investment, reporting, and strategic decision-making.
Module 2: Renewable Energy Data Integration, Preparation and Quality Management
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Integrate SCADA, meter, weather, resource, maintenance, equipment, market, financial, and project datasets while establishing consistent identifiers, timestamps, units, and data structures.
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Apply data-quality procedures for missing values, duplicate records, abnormal measurements, timestamp inconsistencies, sensor errors, outliers, and conflicting operational records.
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Design analytical data models that support plant-level, asset-level, technology-level, portfolio-level, geographic, and time-based renewable performance analysis.
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Develop repeatable data preparation workflows that improve traceability, validation, documentation, refreshability, and confidence in renewable energy performance reporting.
Module 3: Renewable Generation Profiles and Resource Analytics
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Analyze hourly, daily, weekly, monthly, and seasonal renewable generation profiles to understand production patterns, variability, ramping behavior, and operational characteristics.
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Evaluate relationships between solar irradiance, wind conditions, hydrological variables, temperature, environmental conditions, and actual renewable electricity production.
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Compare expected and observed generation patterns to identify abnormal production, resource-driven variation, equipment-related losses, and recurring operational behaviors.
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Construct generation profiles and analytical scorecards for individual plants, technology groups, regions, projects, and renewable energy portfolios.
Module 4: Renewable Asset Performance and Operational Analytics
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Analyze plant and equipment performance using availability, capacity factor, utilization, production variance, downtime, operating hours, losses, and other relevant operational indicators.
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Identify performance degradation, abnormal operating patterns, recurring faults, production losses, equipment underperformance, and operational bottlenecks using historical data.
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Compare assets, sites, turbines, inverters, generators, or other production units using normalized KPIs and appropriate peer-performance benchmarks.
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Develop performance-management workflows that connect analytical findings with maintenance priorities, operational actions, engineering investigations, and management reporting.
Module 5: Renewable Energy Forecasting and Time-Series Intelligence
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Prepare historical generation and resource datasets for short-term, medium-term, and long-term renewable energy forecasting and scenario analysis.
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Apply time-series, regression, weather-driven, and other analytical approaches to forecast renewable generation and evaluate the drivers of forecast variation.
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Measure forecasting performance using appropriate error metrics and investigate systematic bias, extreme forecast errors, seasonal effects, and changing operating conditions.
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Build forecast scenarios that incorporate weather uncertainty, resource variability, asset availability, planned maintenance, operational constraints, and changing renewable capacity.
Module 6: Solar, Wind, Hydro and Multi-Technology Performance Analytics
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Develop technology-specific analytical approaches for solar PV, wind, hydropower, geothermal, biomass, and other renewable generation technologies.
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Analyze solar production using irradiance, temperature, inverter behavior, availability, degradation, clipping, curtailment, and expected-versus-actual generation relationships.
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Analyze wind performance using wind-resource conditions, turbine availability, production curves, operating states, wake-related considerations, and deviations from expected output.
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Analyze hydropower and multi-technology portfolios using resource availability, generation patterns, plant utilization, seasonal conditions, operating constraints, and portfolio-level performance comparisons.
Module 7: Renewable Energy Loss, Curtailment and Performance Optimization Analytics
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Identify technical, operational, environmental, resource-driven, and grid-related causes of renewable generation losses using structured loss categorization and performance analysis.
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Analyze curtailment, downtime, availability losses, resource limitations, equipment constraints, grid restrictions, and other factors affecting expected renewable production.
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Develop root-cause analysis methods that connect production deviations with operational events, maintenance records, weather conditions, equipment status, and grid conditions.
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Prioritize performance improvement opportunities using quantified loss estimates, recurring-event analysis, operational impact, asset criticality, and potential production recovery.
Module 8: Renewable Portfolio, Grid Integration and Scenario Analytics
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Analyze renewable portfolios across technologies, projects, regions, operating conditions, capacity levels, generation profiles, and resource characteristics.
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Evaluate variability, ramping, generation coincidence, storage interactions, curtailment exposure, and other factors relevant to renewable integration and system planning.
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Develop scenarios for renewable capacity expansion, storage deployment, distributed generation, resource variability, operational constraints, and changing electricity demand.
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Use portfolio analytics to support investment analysis, capacity planning, operational strategy, grid integration assessments, and evidence-based renewable energy decisions.
Module 9: Renewable Energy Dashboards, KPIs and Executive Performance Intelligence
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Design renewable energy dashboards covering generation, capacity, availability, capacity factor, production variance, forecast accuracy, losses, downtime, and asset performance.
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Create visualizations for hourly and seasonal production, asset comparisons, resource relationships, anomalies, performance trends, and portfolio-level operational indicators.
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Develop management reporting structures that translate detailed operational analytics into concise insights for executives, asset managers, engineers, planners, and commercial teams.
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Establish automated performance-monitoring concepts using thresholds, alerts, exception reporting, trend analysis, KPI tracking, and recurring analytical workflows.
Module 10: Capstone Renewable Energy Data Analytics and Performance Intelligence Project
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Integrate renewable generation, asset, weather, resource, maintenance, operational, and commercial datasets into a structured end-to-end analytics solution.
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Diagnose generation variability, asset underperformance, production losses, anomalies, forecast deviations, and other performance issues using quantitative analytical methods.
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Develop a renewable energy performance dashboard presenting generation trends, asset KPIs, loss analysis, forecasts, comparisons, exceptions, and actionable management indicators.
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Present an integrated performance intelligence strategy that converts analytical findings into practical recommendations for operations, maintenance, planning, portfolio management, and renewable energy performance improvement.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
