Breaking Down Analytical and Computational Barriers Across the Energy Industry Using Databricks
-
Upload
spark-summit -
Category
Data & Analytics
-
view
280 -
download
0
Transcript of Breaking Down Analytical and Computational Barriers Across the Energy Industry Using Databricks
Breaking Down Analytical and Computational Barriers in Energy Data Analytics
Jonathan FarlandDNV GL Energy
• IntroductionsWho is DNV GL?
• Overview• Data ScienceEnergy Analytics
• DemonstrationStatistical Computing Pilot
• PlansConcepts in Development
• DiscussionQ&A
Agenda
Policy, Advisory and Research
Demand Side Management
Energy Analytics
Load Research Services
Market Research and Program Evaluation
Electricity Distribution Grid
Transmission Distribution ConsumerGeneration Transmission Distribution ConsumerGeneration
WindFarms
PhotoVoltaic
AggregatedUtilityScale2-50MW
UtilityScale
100kW-2MW
DistributedScale
25kW-100kW
ResidentialCommercial&Industrial
DistributionTransmissionGeneration
BulkStorage>50MW
Distribution System
End User
Bulk System
PhotovoltaicWind Farms
Forecasting Approaches
– Similar Day Matching – Statistically Adjusted Engineering
(SAE)– Univariate Time Series (ARIMA)– Multiple Linear Regression – Econometric
– Machine / Statistical Learning– Semiparametric Regression– Artificial Neural Networks– Fuzzy Logic– Support Vector Machines– Gradient Boosting
Additive Semiparametric Model𝑦" = ℎ 𝑡𝑖𝑚𝑒 + 𝑓 𝑤𝑒𝑎𝑡ℎ𝑒𝑟 + 𝛼 𝑏𝑒ℎ𝑎𝑣𝑖𝑜𝑟 + 𝜀"
Short Term Electricity Demand
Time of Year Prevailing
Atmosphere Conditions
Recent Demand Behavior
Additive Semiparametric Model𝑦" = ℎ 𝑡𝑖𝑚𝑒 + 𝑓 𝑤𝑒𝑎𝑡ℎ𝑒𝑟 + 𝛼 𝑏𝑒ℎ𝑎𝑣𝑖𝑜𝑟 + 𝜀"
Short Term Electricity Demand
Time of Year Prevailing
Atmosphere Conditions
Recent Demand Behavior
Emerging Technologies
Photovoltaic Cells (e.g., Solar)
Electric Vehicles Storage Wind
Energy Efficiency Demand Response
21
Load Shifting: Electric Vehicles
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Dem
and
(kW
)
Hour Ending
Standard Rate Electric Vehicle Rate
22
-20,000 40,000 60,000 80,000
100,000 120,000 140,000 160,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Load
(kW
h)
Hour Ending
Forecasted - DR Reduction Forecasted - DR BaselineForecasted - DR Impacted Load Actual DR - Reduction
Load Reduction: Demand Response
Benefits of Big Data from Advanced Metering Infrastructure
ü A deeper understanding of demand and therefore human behavior (think energy efficiency)
ü Cost effective operating costsü Real-time notification of power outagesü Improved System Planning and Reliabilityü Allows for integration of disruptive technologies like
Electric Vehicles
Statistical Computing Pilot
EnergyConsumption
Climatic- Temperature - Humidity- Wind Speed- Solar
Demographic Firmographic
Economic Financial
Energy Efficiency Program Tracking
Grid Infrastructure
Statistical Computing PilotData Diversity
Current Concepts in Development
Weather Normalization at Scale (e.g., California)
Real-time Energy Forecasting Using Statistical Learning and Spark Streaming API
Real-time Customer Sentiment Analysis
Grid Reliability Analysis
Cybercrime Protection of Electricity Grids