© Prof. Dr.-Ing. Wolfgang Lehner |
Time Series Forecasting in Smart Grid Data Management
Matthias Böhm
TU Dresden Database Technology Group
October 26, 2011
124, 165
© Prof. Dr.-Ing. Wolfgang Lehner | | 2
> Disclaimers
I’m not an expert from the energy domain but I will do my best to explain the context of smart grids.
Smart grids are a vision – be aware that we‘re talking about the future!
Data management in smart grids is a huge topic. This talk covers only a small, DB-relevant subset: time series forecasting.
Time Series Forecasting in Smart Grid Data Management
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2
3
© Prof. Dr.-Ing. Wolfgang Lehner | | 3
> Transformation of the Energy Sector
Increasing Energy Demand Increasing world population Increasing industrialization Strong increase in MENA
(middle east, north africa) Moderate increase in EU (Europe)
Problems with Traditional Energy Resources Exhausted fossil resources (ratio 1:106) Risk/share of nuclear power
General Political Goal: Increased Integration of RES (Renewable Energy Sources) Different technologies Centralized and Decentralized
Time Series Forecasting in Smart Grid Data Management
[Hans Müller-Steinhagen: „DESERTEC: Strom aus der Wüste für eine Klima und Ressourcen schonende Energieversorgung Europas”, TU Dresden, 2010]
(TUD @ DUN since 07/2011)
© Prof. Dr.-Ing. Wolfgang Lehner | | 4
> Example Decentralized Generation
Photovoltaic Highest production
per required space EEG Sachsen: ca 911 kWh p.a. / kWp
Parents‘ House (9.8 kWp)
Sister‘s House (8.0 kWp)
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 5
> Types of Renewable Energy Sources
Storable Energy Hydro power Biomass Solarthermal power Geothermal power
Fluctuating Energy Wind power Photo Voltaic Waves / Tides
Fluctuations require balancing energy demand/supply
[Hans Müller-Steinhagen: „DESERTEC: Strom aus der Wüste für eine Klima und Ressourcen schonende Energieversorgung Europas”, TU Dresden, 2010]
CRES PV
22 kWp (Greece)
CRES Windpark 2410 kWp
(Greece)
1 Week (20.10-26.10)
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 6
> Outline
Motivation and Introduction Background Smart Grids MIRABEL Project
Time Series Forecasting in DBMS Background Advanced Analytics in DBMS Background Model-Based Forecasting Forecasting in Relational DBMS
Forecast Query Optimization Techniques Hierarchical Forecasting Context-Aware Model Maintenance Publish Subscribe Forecast Queries
Conclusion
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 7
>
Background Smart Grids
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 8
> Future Vision: Smart Grids
Smart Grids Increased flexibility of energy networks via ICT (monitor, control) Goals: more RES, active customer involvement, balancing demand/supply Example: EU European SmartGrids Technology Platform (set up in 2005)
http://www.smartgrids.eu/documents/vision.pdf
Peer-to-Peer
Micro-Grids
Large Power Plants
Virtual Power Plants
Data Management Challenges
Time Series Forecasting in Smart Grid Data Management
Smart Meter: foundation for
smart grids (bi-directional
communication)
© Prof. Dr.-Ing. Wolfgang Lehner | | 9
> Overview Data Management Challenges
Large-Scale Distributed Systems Number of stakeholders, number of nodes, amount of data
High Availability / Fault Tolerance Basically available, soft state, eventual consistent
Near-Realtime Data Synchronization and Integration High update rates, low latency, protocol/schema/format heterogeneity
Advanced Analytics Time series forecasting, scheduling/ balancing, classification, clustering, association
rule mining, complex event detection
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 10
> People
Prof. Dr.-Ing. Wolfgang Lehner
Dr.-Ing. Matthias Böhm [email protected]
Dipl.-Inf. Ulrike Fischer [email protected]
Dipl.-Medien-Inf. Lars Dannecker [email protected]
http://www.mirabel-project.eu/
Time Series Forecasting in Smart Grid Data Management
EU FP7 project
© Prof. Dr.-Ing. Wolfgang Lehner | | 11
> Some Smart Grid Research Projects
Project Description
ADDRESS Real-time communication architecture enabling active demand and real-time request responses
AEOLUS Real-time prediction and distributed control of large-scale off-shore wind farms
EDISON* Electric vehicles as storage in distributed and integrated markets and open networks
EU DEEP Business solutions for enhancing distributed energy resources via a demand-pull approach
FENIX Aggregation of distributed energy resources into large scale virtual power plants
MeRegio Regions with power supply systems that are optimized with respect to their greenhouse gas emissions
MIRABEL* Balancing of energy demand and supply based on specified consumption and production flexibilities
MORE MICROGRIDS
Multi-micro grids management operation systems and centralized/ decentralized control strategies
Smart House- /Smart Grid
Inter-influences of smart houses and smart grids and hierarchical control concept
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 12
> Balancing Potential
MeRegio Data (Karlsruhe/Stuttgart) Elasticity to dynamic pricing
of real customers Three levels (SNT, NT, HT),
varying during the day, announced day-ahead
Significant differences to control group of up to 10%
Additional Conclusions (SAP Research User Study, E-Energy) Acceptance depends on device (e.g., dish washer, tumble dryer) Acceptance if comfort not lost and user keeps full control
Realistic time shifts: 0.5 to 3 hours. Main motivator: financial benefit, ecological benefit nice-to-have Fear of inapplicability and loss of flexibility
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 13
>
MIRABEL Project
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 14
> MIRABEL Consortium
Project start: 01/2010 Project end: 12/2012
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 15
> MIRABEL Approach – Flex Offers
Consumer and producers (households, SMEs) Have schedulable (flexible) demand and supply flex-offers
Flexibilities Time (flexibility interval), Amount of electricity (profile), and/or Price
Time Series Forecasting in Smart Grid Data Management
kW
t
8pm earliest starting time
6 am latest starting time
8 am
2h
Profile
Flex-offer Earliest starting time: 8pm
Latest Starting time: 6am
Profile: Washing machine, 30°, normal
Price: 15ct/kWh or less
Flexibility interval
© Prof. Dr.-Ing. Wolfgang Lehner | | 16
> MIRABEL Approach – Big Picture
Supply Demand
Scheduling
FlexOffers with flexibilities
(time, amount)
Forecasting Aggregation BG 11 BG 12
BG 1
MBA
Balance Group 2
Use Cases (demand and supply):
- Production schedules - Electric heat pumps, - Electric vehicles, - Washing machines, - Dryer - Dishwashers, - Photo voltaics, - Urban wind, and - Micro combined heat and power
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 17
> Example Use Case: RES Integration
Time Series Forecasting in Smart Grid Data Management
Demand
Supply
Flex-offers
Non-schedulable demand
Non-schedulable RES
© Prof. Dr.-Ing. Wolfgang Lehner | | 18
> Motivation Forecasting
Objectives Accurate forecasting of energy demand and supply System architecture integration Efficiency, robustness, scalability
Flex Offers
Flex Offers
Supply Demand Scheduling
RES Supply Forecasts
Demand forecasts
FlexOffer Forecasts
Accurate/efficient forecasting as precondition for scheduling
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 19
> Outline
Motivation and Introduction Background Smart Grids MIRABEL Project
Time Series Forecasting in DBMS Background Advanced Analytics in DBMS Background Model-Based Forecasting Forecasting in Relational DBMS
Forecast Query Optimization Techniques Hierarchical Forecasting Context-Aware Model Maintenance Publish Subscribe Forecast Queries
Conclusion
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 20
>
Advanced Analytics in DBMS
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 21
> MAD Skills
Magnetic „Attract data and
practitioners“ Use all available data
sources independent of their quality
Agile „Rapid iteration: ingest, analyze, productionalize“ Continuous and rapid evolution of physical and
logical structures ELT (Extraction, Loading, Transformation)
Deep „Sophisticated analytics in Big Data“ Extended algorithmic runtime environment Ad-hoc advanced analytics and statistics
Time Series Forecasting in Smart Grid Data Management
[Jeffrey Cohen, Brian Dolan, Mark Dunlap, Joseph M. Hellerstein, Caleb Welton: MAD Skills: New Analysis Practices for Big Data. PVLDB 2(2):1481-1492 (2009)]
© Prof. Dr.-Ing. Wolfgang Lehner | | 22
> Data Mining Techniques
Predictive Techniques Descriptive Techniques
Nominal Data
Numerical Data
Classification
Clustering
(Outlier Detection)
Time Series Forecasting
(Recommendations)
T1 T2
T3 T4
Regel:
Association Rule Mining
Sampling
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 23
> Time Series Forecasting in DBMS
Time Series Forecasting in Smart Grid Data Management
Advanced Analytics / Forecasting in DBMS
Analysis (e.g., R, SPSS)
DBMS
Traditional (on top)
Full Integration
Predictive DBMS
DBMS
DBMS UDF UDF
Partial Integration (extension
functionalities) (bi-directional)
Analysis (e.g., R, SPSS)
[SIGMOD’10] [VLDB‘11]
[VLDB’07] [VLDB‘08] [CIDR‘11] [ICDE‘12]
[Microsoft’11] [Oracle‘11]
other commercial DBMS
(general/special- purpose)
[SIGMOD’06] [SIGMOD‘08]
© Prof. Dr.-Ing. Wolfgang Lehner | | 24
>
Model-Based Forecasting
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 25
> Overview Model-Based Forecasting
Forecast Model Statistical time series description (model)
(Recursive) Forecasting Process Model Identification Model Estimation Forecasting and Model Update Model Evaluation Model Adaptation
Time Series Forecasting in Smart Grid Data Management
Model Identification
Model Estimation
Forecasting
Model Evaluation
Model Adaptation
Model Maintenance
Time Series Data
Model Type AR(2)
Model Parameters φ1=0.55, φ2=0.45
New Time Series
Values Ui
Forecasting Values Fi+h
Model Creation Model Usage
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> Model Identification
Forecast Model Types / Classes
Time Series Forecasting in Smart Grid Data Management
Base Forecast Models
Exponential Smoothing
Machine Learning
(Auto)Regression
Domain-Specific Extensions
HWT (Single-Equation)
EGRV (Multi-Equation)
BN (Bayesian Networks)
SVM (Support Vector Machines)
SVR (Support Vector Regression)
ANN (Artificial Neural Networks)
Black-Box Gray-Box
White-Box
AR MA
ARMA ARIMA
SARIMA ARMAX
MLR (Multiple Linear Regression)
SESM (Single Exponential Smoothing)
DESM (Double Exponential Smoothing)
TESM / HoltWinters
(Triple Exponential Smoothing)
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> Forecast Model Types
EGRV-Model (Engle, Granger, Ramanathan, and Vahid-Arraghi)
Multi-equation autoregressive model (ensemble) White-box model tailor-made for energy demand
Core Idea: Time Series Decomposition
Simple models with many specific variables (30-50)
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 28
> Model Estimation
Problem Instantiate a forecast model w.r.t.
meta model and training data set
Example Forecast Model Type AR(2):
Error Metric: MSE
Horizon h=1 Meregio Customer 40
Energy Demand
Parameter Estimator L-BFGS-B
Time Series Forecasting in Smart Grid Data Management
2211ˆ −− ⋅+⋅= ttt yyy φφ
( )∑=
−n
iii yy
n 1
2ˆ1
eMSE=827,354.4
0.23
0.56
eMSE= 211,204.7
© Prof. Dr.-Ing. Wolfgang Lehner | | 29
> Model Usage
Forecasting Use the estimated forecast model Create h forecast values (forecast horizon) Update model state for new measurements (e.g., exponential smoothing)
Example Forecast (EGRV) SMAPEvshort
=0.0021 SMAPElong
=0.0755
Time Series Forecasting in Smart Grid Data Management
21 23.056.0ˆ −− ⋅+⋅= ttt yyy
30min 1year
© Prof. Dr.-Ing. Wolfgang Lehner | | 30
> Model Maintenance
Model Evaluation Goal: Trigger model adaptation only if necessary
Fixed Interval Techniques (# updates, time interval)
Continuous Evaluation Techniques (threshold, on-demand)
Model Adaptation Goal: Adapt the forecast model to the changed time series (if necessary)
Model Re-Identification Model Re-Estimation (old model as start point)
Time Series Forecasting in Smart Grid Data Management
h=2
© Prof. Dr.-Ing. Wolfgang Lehner | | 31
>
Forecasting in DBMS
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 32
> Example Forecast Query
Time Series Forecasting in Smart Grid Data Management
SQL Forecast Query
Logical Query Plan Forecast operator Ψ
(create/reuse + forecast)
SELECT S_Date, S_Qty FROM Article, Sales WHERE A_Anr = S_Anr AND A_Name = ’Article A’ FORECAST 2
… S_Anr S_Date S_Qty
… 1 2011-10-24 6
… 1 2011-10-25 5
… 2 2011-10-26 1
… 1 2011-10-26 7
A_Anr A_Name …
1 Article A …
2 Acticle B …
S_Date SUM
2011-10-24 6
2011-10-25 5
2011-10-26 7
2011-10-27 6
2011-10-28 6.5
Article
Result
⋈S_Anr=A_Anr
Sales
Ψk=2
Q:
σ A_Name= ‚Article A'
πS_Date, S_QTY
© Prof. Dr.-Ing. Wolfgang Lehner | | 33
> Forecast Query Compilation
Logical Plan Rewriting Cost model: accuracy and efficiency Example
Physical Plan Rewriting Operator alternatives (create model, scan model, etc) Operator parameterization (model type, estimator, etc)
Time Series Forecasting in Smart Grid Data Management
Increased Model Creation Efficiency Possibly Decreased Model Accuracy (Increased Plan Costs (join, project),
Model reuse possibilities)
Sales2
⋈Sales1.Date=Sales2.Date
πSales1.Date, Sales1.Amount – Sales2.Amount
Sales1
Ψk=2 Ψk=2
Sales2
⋈Sales1.Date=Sales2.Date
πSales1.Date, Sales1.Amount – Sales2.Amount
Sales1
Ψk=2
© Prof. Dr.-Ing. Wolfgang Lehner | | 34
> Schema Architecture
From 3-Layer to 4-Layer Schema Architecture
Time Series Forecasting in Smart Grid Data Management
App
T
Storage
External Schema
Internal Schema
Conceptual Schema
Logical data independence
Phyiscal data independence
T
Storage
Conceptual (Statistical)
Schema
Internal Schema
Conceptual (Data)
Schema
Logical data independence
Phyiscal data independence
App External Schema
T
Logical model independence
Transparency allows for optimizations (accuracy/efficiency)
3-Layer Schema Architecture (ANSI/SPARC)
4-Layer Schema Architecture
© Prof. Dr.-Ing. Wolfgang Lehner | | 35
>
Physical Models ( )
Schema Architecture in Depth
Time Series Forecasting in Smart Grid Data Management
Index Structures
Logical Access Paths
Physical Access Paths
B+-Tree BitMap Compression
Partitioning
Materializations
Base Relations R S
…
…
Conceptual (Statistical)
Schema
Internal Schema
Conceptual (Data)
Schema
Model/Time Series Index Structures
Configurations
Logical Models
T
M1 M2 M3
M1 M11 M12 := +
M11 AR(2), MSE, (R⋈ (σ Name=AS)),
L-BFGS-B, h=6, τSMAPE=0.1
Model Index
Skip-Lists
Similarity Indexes
Logical Computation
Schemes
Physical Computation
Schemes
© Prof. Dr.-Ing. Wolfgang Lehner | | 36
> Outline
Motivation and Introduction Background Smart Grids MIRABEL Project
Time Series Forecasting in DBMS Background Advanced Analytics in DBMS Background Model-Based Forecasting Forecasting in Relational DBMS
Forecast Query Optimization Techniques Hierarchical Forecasting Context-Aware Model Maintenance Publish Subscribe Forecast Queries
Conclusion
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 37
> Overview MIRABEL Forecasting Approach
Forecast Queries
Forecasting and Maintenance
Streams of New Measurements
Forecasting Component
Qi
Scheduling, Aggregation, Monitoring
Ui
Energy Producer (Supply)
Energy Consumer (Demand)
Forecast Queries
Forecast Models
Model Evaluation Model Adaptation
Domain-Specific Forecast Models:
EGRV, HWT
Publish/Subscribe Forecast Queries
Partitioning and Parallelization Physical
Design (Hierarchies,
Horizons, Sampling)
Weather Integration
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 38
>
Hierarchical Forecasting
[Ulrike Fischer, Matthias Böhm, Wolfgang Lehner: Offline Design Tuning for Hierarchies of Forecast Models.
BTW 2011:167-186]
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 39
> Problem and Solution Overview
Time Series Forecasting in Smart Grid Data Management
1. Deploy once and use many times 2. Keep and maintain a subset of models
(Physical Design of FM Hierarchies) DWH Model Pool
Query Interface
Updates Forecast Queries
HTC
HD2
Mobiles
Smart
Nokia
SELECT date, SUM(sales) FROM facts WHERE pgroup = „HTC“ GROUP BY date FORECAST 1 month
Scan
Aggregate
BuildModel
Forecast
facts
Forecast
MHTC Forecast
MHD2
Forecast
MSmart
Aggregation
Forecast
MMobiles
DisAgg
Key
1. Aggregation
2. Disaggregation Model Advisor
Workload Preference
Create Configuration
Analyze
Error + Cost
Configuration
© Prof. Dr.-Ing. Wolfgang Lehner | | 40
> Optimization Problem
Configuration CW
Find best set of forecast models for a multi-dimensional aggregation hierarchy and given workload
Cost Model Efficiency: Maintenance Cost BW (# FM in CW) Accuracy: Configuration Error EW (sum of errors over W using best)
Optimization Objective Linearized costs
Optimization Algorithms Problem: Exponential search space Greedy Algorithm (start bottom-up, monotonic maintenance costs) Heuristics (recursive, decomposition, correlation, disagg error)
Time Series Forecasting in Smart Grid Data Management
]1,0[ with )1( minmax
∈
−+ ααα
BB
EE W
T
W
CW
Weighted Accuracy
Weighted Efficiency
© Prof. Dr.-Ing. Wolfgang Lehner | | 41
> Experimental Evaluation
Complete (C) All models, only direct forecasts
Bottom-Up (B) Only models at level one, others use aggregation
Top-Down (T) Only one model for top element, others use disaggregation
Greedy (G)
Time Series Forecasting in Smart Grid Data Management
Maintenance Costs Accuracy
© Prof. Dr.-Ing. Wolfgang Lehner | | 42
>
Context-Aware Model Maintenance
[Lars Dannecker, Robert Schulze, Matthias Böhm, Wolfgang Lehner, Gregor Hackenbroich: Context-Aware Parameter Estimation for Forecast Models in the Energy Domain.
SSDBM 2011:491-508]
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 43
> Problem and Solution Overview
Problem of Evolving Energy Time Series Optimal parameters change over time (with seasonal behavior) Rough search space (many local minima)
Basic Idea Model adaptation by reusing forecast models
w.r.t. current context
Case-Based Reasoning (Learning how to solve new problems from past experience)
Retain: Save FM with their context
Retrieve: Search FM for current context
Revise: FM refined by local/global optimization Time Series Forecasting in Smart Grid Data Management
Problem-Solution Case Base
Revise Retain
Retrieve
}{ ip}{ ip}{ ip
}{ ip
Local/global optimization
}{ ip′
© Prof. Dr.-Ing. Wolfgang Lehner | | 44
> Model History Tree
Decision Tree Decision node:
splitting attr, value Leaf node:
forecast models Splitting attribute (highest PIQR), splitting value (partitioning median)
Experimental Evaluation
Time vs. Accuracy; TSESM, UK Demand Time vs. Accuracy; EGRV, UK Demand
Most beneficial for complex forecast models
Robust w.r.t. different models, error metrics,
data sets …
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 45
>
Publish-Subscribe Forecast Queries
[Ulrike Fischer, Matthias Böhm, Wolfgang Lehner, Torben Bach Pedersen: Publish-Subscribe Forecast Queries.
submitted for publication]
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 46
> Problem and Solution Overview
Problem Applications often continuously require forecast
values and do complex processing Forecast queries inefficient if complex algorithm
or only small changes
Publish-Subscribe Forecast Queries (PSFQ) Notify application for significant new forecast values
Time Series Forecasting in Smart Grid Data Management
Example MIRABEL Scheduling (Goal: Supply – Demand = 0)
Forecasting
Scheduling
Supply Demand
Subscribe Publish
SELECT datetime, energydemand FROM customers WHERE customer_id = 30 FORECAST 2 THRESHOLD 0.1
Different Possibilities 1) Send requested values h (many horizon violations) 2) Send max values h+max (many theshold violations) 3) Send h+k values
© Prof. Dr.-Ing. Wolfgang Lehner | | 47
> Optimization Problem
Horizon Extension k
Find best horizon extension for given time series and subscriber
(Subscriber) Cost Model Horizon violation incremental costs FI
Threshold violation complete costs FC
Optimization Objective Determine k(s) that minimize overall costs
Optimization Algorithms
Time Series Forecasting in Smart Grid Data Management
horizon extension
+⋅
−
+∆
++
∆≥+=
=
∆
=∆∑
otherwise )1(11
)(
)(
with
,
1,
kFk
DkhF
DkkhFC
CC
IC
C
Dk
n
iDktotal
ii
ii
Offline-Static Offline-Time Slices Online
© Prof. Dr.-Ing. Wolfgang Lehner | | 48
> Experimental Evaluation
Offline Algorithms
Real-World Experiment
Time Series Forecasting in Smart Grid Data Management
Offline-Static Offline-TimeSlice
© Prof. Dr.-Ing. Wolfgang Lehner | | 49
> Outline
Motivation and Introduction Background Smart Grids MIRABEL Project
Time Series Forecasting in DBMS Background Advanced Analytics in DBMS Background Model-Based Forecasting Forecasting in Relational DBMS
Forecast Query Optimization Techniques Hierarchical Forecasting Context-Aware Model Maintenance Publish Subscribe Forecast Queries
Conclusion
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner | | 50
> Conclusions
Smart Grids Problems of traditional energy sources Flexible energy networks via ICT (RES, customers, balancing) Data management challenges (e.g., time series forecasting)
Time Series Forecasting Time series forecasting required for balancing Advanced analytics / time series forecasting in DBMS (functionality, transparency) Transparency allows for optimization (efficiency, accuracy) Forecast query processing and optimization techniques Domain-specific forecasting models and
optimization techniques
Lots of optimization potential and directions for future research
Time Series Forecasting in Smart Grid Data Management
© Prof. Dr.-Ing. Wolfgang Lehner |
Time Series Forecasting in Smart Grid Data Management
Matthias Böhm
TU Dresden Database Technology Group
October 26, 2011
124, 165, 206?
© Prof. Dr.-Ing. Wolfgang Lehner | | 52
>
Adaptive Re-Optimization Project: GCIP Dissertation “Cost-Based Optimization of Integration Flows” (03/2011) First adaptive, cost-based re-optimizer for EAI, ETL, MOM systems
In-Memory Indexing / Query Processing Project: DEXTER Generalized prefix trees with transaction management Query processing on generalized prefix trees
Time Series Forecasting in DMS Project: MIRABEL, FFQ Efficient time series forecasting for evolving time series Forecast query processing and physical design tuning
Other Research Projects Resiliency-aware data management Architecture-aware adaptive query processing (Database programming languages)
My Background
Time Series Forecasting in Smart Grid Data Management