Murphy

Group 5:

Bhargav Bhatt (20131008)

Darshit Paun (20131010)

Jalaj Malhotra (20131015)

Nisarg Shah (20131030)

Rohan Bharadwaj(20131043)

Vishal Nadgir (20131059)

Vikas Gupta (20131058)

Solar energy

Wind energy

• Overall Industry value chain

• Business intelligence

• Utilization of BI in each of the stages of value chain

• Benefits of BI

• Analytics

• Implementation analytic in each of the stages of value chain

• Benefits of Analytics

• Products available in the market in BI & Analytic space for this industry

• Process we propose for implementation BI & Analytic solutions in the value chain

Introduction: Business

Intelligence

Demand Intelligence

Risk Intelligence

Asset Intelligence

Customer Service Intelligence

Root Cause analysis

Forecasting

Strategy Planning

Data visualization

Market Opportunities

Policy trend analysis

Energy-focussed scenario modeling

Analytics is the science of examining raw data with the purpose of drawing conclusions about that information.

Analytics is used to make better business decisions and to verify or disprove existing models or theories.

Mining sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships.

Analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.

The science is generally divided into:

Exploratory data analysis (EDA), where new features in the data are discovered.

Confirmatory data analysis (CDA), where existing hypotheses are proven true or false.

Qualitative data analysis (QDA), conclusions are drawn from non-numerical data like words, photographs or video.

Sophisticated analytics are enabling renewable energy companieswith deeper insight which helps them better manager the variablenature of wind and solar, and more accurately forecast the amount ofenergy that can be redirected into the power grid or stored.

Provide customers with irradiance data for their location.

Short-Term Photovoltaic Power Predictions (meteorological forecasts from numerical weather prediction (NWP) models)

Systematic Optimized Strategy for Solar Energy Supply Forecasting (hybrid of weather and energy forecast models)

Fault Detection of Large Amounts of Photovoltaic Systems

Active solar monitoring

Real-time Solar Energy Output tracking

Real-time Energy Consumption monitoringIn-time Fault and Diagnosis Alerts

Monthly regular Performance Report

Wind turbines are big and expensive machines, so keeping them running smoothly helps keeping their operational cost down.

The sensor data generated by the turbine can help achieving this – by analysing it, you can spot potential failures earlier. The longer the warning period before a part fails, the better you can prepare for it.

Preventive maintenance saves you money when you have:

1. Shorter downtime and less lost production

2. Better planning of people and materials

3. Cheaper repairs

•Wind Energy Prediction

•Meteorological mast data checks;

•Energy yield calculation using industry standard software tools;

•Calculations of shadow flicker, noise impact, etc;

•Fleet power curve surveys identifying abnormal performance,

underperformance, curtailment and other features indicated by SCADA data

including identification of changes in time and differences from turbine to

turbine;

•Detailed wind farm asset performance analysis against budget including

monthly or other breakdown of the component reasons for deviation from

budget such as availability, grid outage, wind conditions, over performance,

underperformance and curtailment, etc.;

•Energy yield impact estimation to evaluate the efficiency impact of particular

events such as blade cleaning or turbine parameter changes;

•SCADA data investigations to evaluate the impact of downtime due to

particular wind turbine component or outage types;

•LIDAR investigations in order to check whether the turbines are being

subjected to abnormal wind conditions causing significant underperformance,

due to complex terrain, nearby forestry or other reasons;

1.Security and Theft Detection

2.Preventive Equipment

Maintenance

3.Demand Response Management

4.Field Service Management

5.Real-Time Customer Billing &

Provisioning

How much demand for electricity

will there be and when?

Which transformer may blow next

week? (So let's perform

maintenance on it this week.)

Where should we set up new plant

to achieve the best results?

Which type of material is better for

manufacturing and why?

IHS company –Wind Energy

Products across the value chain.

IHS company – Emerging products

in the Renewable Energy Segment.

BI in a Wind Turbine by Siemens

IBM-Hybrid Renewable Energy

Forecasting" (HyRef) ::

IBM-Hybrid Renewable Energy

Forecasting" (HyRef) ::

IBM-Hybrid Renewable Energy

Forecasting" (HyRef) ::

Virtual Irradiance solar

analytics developed by Locus

Technologies

The Virtual Irradiance solar

analytics tool will combine NASA

satellite data, government weather

station data and data from Locus'

network of 40,000 systems which it

monitors in North America to

provide customers with irradiance

data for their location.

The tool provides data for a one

square kilometre location

anywhere in the continental United

States, in fifteen minute intervals.

Locus says that it is as accurate as

on-site weather sensors over a

one-month period.

http://www.kwhanalytics.com/

http://www.lavastorm.com/solutions/by-industry/utilities/

http://heliopower.com/predictenergy/

http://www.geovisual-analytics.com/

http://www-03.ibm.com/press/us/en/pressrelease/41310.wss

http://www.ibmbigdatahub.com/video/optimizing-operations-countering-fraud-and-threats