IMPACT MODELS AND DATA MATTEO DE FELICE
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1 IMPACT MODELS AND DATA MATTEO DE FELICE
2 What is an impact model
3 What is an impact model
4 What is an impact model
5 What is an impact model Modelling the influence of something on something else
6 What is an impact model Modelling the influence of something on something else Usually two different domains (e.g. economics and industrial production, climate and energy sector, )
7 What is an impact model Why? How to build an impact model? Two approaches: modelling underlying processes or modelling observed input/outputs
8 ESSENTIALLY, ALL MODELS ARE WRONG, BUT SOME ARE USEFUL. George E. P. Box (English statistician)
9 Boxes White box Organised knowledge Black box Gray box Zero knowledge/ many observations and measures Some knowledge and some data
10 Right tools
11 Right tools Wolpert No Free Lunch theorem: best model doesn t exist considering all possible problems
12 Right tools Wolpert No Free Lunch theorem: best model doesn t exist considering all possible problems Exploring the trade-off between time-to-market and efficiency/performances
13 Right tools Wolpert No Free Lunch theorem: best model doesn t exist considering all possible problems Exploring the trade-off between time-to-market and efficiency/performances Performance 0 time-to-market
14 Right tools Wolpert No Free Lunch theorem: best model doesn t exist considering all possible problems Exploring the trade-off between time-to-market and efficiency/performances Performance 0 Random guess (dice?) time-to-market
15 Right tools Wolpert No Free Lunch theorem: best model doesn t exist considering all possible problems Exploring the trade-off between time-to-market and efficiency/performances Genie s lamp Performance 0 Random guess (dice?) time-to-market Worthless effort
16 Right tools Wolpert No Free Lunch theorem: best model doesn t exist considering all possible problems Exploring the trade-off between time-to-market and efficiency/performances Genie s lamp Performance 0 Random guess (dice?) time-to-market Worthless effort
17 Modeling software Spreadsheet software (e.g. Microsoft Excel) Code programming (C/C++, Python) Statistical computing environments (e.g. MATLAB, R) Performance MATLAB R Excel Low-level code High-level code 0 time-to-market
18 Modeling software Spreadsheet software (e.g. Microsoft Excel) Code programming (C/C++, Python) Statistical computing environments (e.g. MATLAB, R) Performance MATLAB Low-level code Cost R Excel Flexibility High-level code 0 time-to-market
19 Summary
20 Summary To take decisions we need estimates and predictions
21 Summary To take decisions we need estimates and predictions To generate predictions we need models
22 Summary To take decisions we need estimates and predictions To generate predictions we need models To build models we need data and information
23 DRIP & Big Data DRIP (Data Rich Information Poor) era (Big Data) We need tools to deal with high-dimensional and heterogeneous data Data factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation ( Merriam-Webster)? Information knowledge obtained from investigation, study, or instruction ( Merriam-Webster)
24 Data vs Information Precise formally-defined Accurate Data Information Useful context-related Meaningful
25 It is often said that we suffer from information overload, whereas we actually suffer from data overload. The problem is that we have access to large amounts of data containing relatively small amounts of useful information. INDEPENDENT COMPONENT ANALYSIS A Tutorial Introduction James V. Stone
26 Impact Models How the weather affects agriculture in a specific region? (weather/climate agricultural system) How the weather affects the solar power production? (weather/climate energy sector)
27 Impact Models GCM/ RGCM Climate Data Compare with observations Skill & Error Measures
28 Impact Models GCM/ RGCM Climate Data Impact Models Impact Information Compare with observations Skill & Error Measures
29 Drought Early Warning
30 Drought Early Warning From precipitation/evotranspiration (water balance) to crop production
31 Drought Early Warning From precipitation/evotranspiration (water balance) to crop production to the (estimated) number of people affected by drought
32 Drought Early Warning From precipitation/evotranspiration (water balance) to crop production to the (estimated) number of people affected by drought Objectives: model must be reliable, timely and tailored
33 WRSI: Crop Water Requirement Satisfaction Index CWRSI = q belg WRSI belg + q meher WRSI meher RW RSI = P P CWRSI P P N = N 0 + K [log(w m W 0 ) log(rw RSI W 0 )]
34 Impact Model Calibrated on observed data using empirical rules and statistical methods Monte Carlo methods to deal with the uncertainty
35 Solar Power
36 Approaches
37 Approaches Modelling PV technology (isc, ki, kv, )
38 Approaches Modelling PV technology (isc, ki, kv, ) Black-box model observing input (weather) and output (production)
39 Approaches Modelling PV technology (isc, ki, kv, ) Black-box model observing input (weather) and output (production) Accuracy Feasibility Uncertainty? What if 100 PV plants? panels?
40 Data Mining Approach
41 Data Mining Approach Extraction of implicit and potentially useful information from data Automated search (software) Main difficulties: Defining interestingness Spurious and accidental coincidences (exceptions) Missing data and noise
42 Modelling Methods Experience Rule-of-thumbs and good practises Statistics Machine Learning methods Forecasting Analysis of past events Control Anomaly Detection Why?
43 Tables Rudimentary and simple Look up the appropriate inputs Table 1.2 Weather Data Outlook Temperature Humidity Windy Play Sunny hot high false no Sunny hot high true no Overcast hot high false yes Rainy mild high false yes Rainy cool normal false yes Problem with large (real-world) datasets What about continuous variables?
44 Linear Models y = a 1 SSR + a 2 CC + a 3
45 Linear Models y = SSR CC Correlation of 0.84 Can we trust this model?
46 Regression Trees yes 1 SSR < 0.5 no 2 CV >= SSR < SSR < 0.19 SSR < CV >= library rpart rtree = rpart(pv_prod ~ SSR + CV, data = my_obs)
47 Regression Tree #2 output day
48 Linear vs Tree lm(formula=output~ssr+cv,data=df) SSR: CV vector rpart(formula=output~ssr+cv,data=df) SSR: CV SSR CV SSR CV
49 Linear vs Nonlinear
50 Best Tools
51 Best Tools 1. Data Retrieval
52 Best Tools 1. Data Retrieval 2. Data Processing
53 Best Tools 1. Data Retrieval 2. Data Processing 3. Model Building
54 Best Tools 1. Data Retrieval 2. Data Processing 3. Model Building 4. Data Visualization
55 Best Tools 1. Data Retrieval ecomsudg 2. Data Processing 3. Model Building 4. Data Visualization
56 Best Tools 1. Data Retrieval 2. Data Processing 3. Model Building 4. Data Visualization ecomsudg rnoaa
57 Best Tools 1. Data Retrieval 2. Data Processing 3. Model Building 4. Data Visualization ecomsudg downscaler rnoaa
58 Best Tools 1. Data Retrieval 2. Data Processing 3. Model Building 4. Data Visualization ecomsudg downscaler rnoaa plyr/dplyr
59 Best Tools 1. Data Retrieval 2. Data Processing 3. Model Building 4. Data Visualization ecomsudg downscaler e1071 rnoaa plyr/dplyr
60 Best Tools 1. Data Retrieval 2. Data Processing 3. Model Building 4. Data Visualization ecomsudg downscaler e1071 rnoaa plyr/dplyr rpart
61 Best Tools 1. Data Retrieval 2. Data Processing 3. Model Building ecomsudg downscaler e1071 rnoaa plyr/dplyr rpart 4. Data Visualization shiny, ggplot2
62 Meanwhile in the real world
63 Meanwhile in the real world.grib.nc metadata You expect this
64 Meanwhile in the real world.grib.nc metadata You expect this but you get this!
65 Dealing with the complicatedness
66 Dealing with the complicatedness Be patient
67 Dealing with the complicatedness Be patient Be methodical (frommessydatatonetcdf.r)
68 Dealing with the complicatedness Be patient Be methodical (frommessydatatonetcdf.r) Be reproduciple"
69 Data Visualization Scientist Programmer Data Analyst Scientific Papers Data Visualisation Stakeholders
70 Rstudio (Server) Open
71 Rstudio (Server)
72 Rstudio (Server)
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