Machine Learning with Neural Networks. J. Stuart McMenamin, David Simons, Andy Sukenik Itron, Inc.
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1 Machine Learning with Neural Networks J. Stuart McMenamin, David Simons, Andy Sukenik Itron, Inc.
2 Please Remember» Phones are Muted: In order to help this session run smoothly, your phones are muted.» Full Screen Mode: To make the presentation portion of the screen larger, press the expand button on the toolbar. Press it again to return to regular window.» Questions: If you want to ask a question, type your question in the chat or Q&A boxes. We will address it as soon as we can.» Recording will be posted online. A link to download the recording and pdf when it is available.
3 Overview» Focus is on hourly load modeling» Question 1: How can neural network models be used to identify nonlinear relationships?» Question 2: How can we use this knowledge to build stronger hourly regression models?» Question 3: Can this approach be used to build accurate and robust hourly models?
4 Daily Energy vs Daily Average Temperature
5 Daily Weather Response HDD60 = Max(60 AvgDB, 0) CDD60 = Max(AvgDB 60, 0)
6 Specification 1. Base Model
7 Specification 1: Base Model
8 Degree-Day Variable Calculations (HR 17) Avg DB CDD HDD
9 Specification 1: Model Results
10 Neural Network Background
11 Linear Model Y Y a bx y 1 y 0 a Slope = Derivative dy y1 y b 0 dx x x 1 0 x 0 x 1 X
12 Nonlinear Model Y Local Slope = Derivative dy y1 y 0 dx x x 1 0 Y f (X) y 1 y 0 x 0 x 1 X
13 Neural Network Model Specific: where: General Form: Y G X, b u t 3 3 t 2 2 t t 1 X a X a X a a e 1 1 H t 3 3 t 2 2 t t 2 X X X b b b b e 1 1 H t t 2 2 t t u H B H B B Y Specific Form 2 Nodes, 3 X s: Flexible nonlinear functional form Identifies important nonlinearities Identifies important variable interactions
14 Neural Network Specification t t 2 2 t u H B H B B t Y t 3 3 t 2 2 t t 1 X a X a X a a e 1 1 H t 3 3 t 2 2 t t 2 X b X b b X b e 1 1 H Output Layer Hidden Layer Input Layer H 1 H 2 Y X 1 X 2 X 3 Feed forward Neural Net with a Single Output (Y) One Hidden Layer with Two Nodes (H 1 and H 2 ) Logistic Activation Function in the Hidden Layer Linear Activation Function in the Output Layer
15 Logistic Activations are Nonlinear 1 exp Z HX where Z a0 a1x1 a2x exp Z 1 exp Z H = exp(z)/(1+exp(z)) Z = Weighted Sum of X s
16 Why This Form is Useful
17 Why This Form is Useful
18 Derivatives h t h t h j h, h t j t 2 Z exp 1 Z exp a B X Ŷ t h t h t h t h h h t j t X Z Z H B X Ŷ t t h h 0 t u H B B Y h j t j h,j h,0 t h X a a Z Neural Network Logic Partial Derivatives t h t h t h exp Z H 1 exp Z
19 Model Derivatives in MetrixND (F Tab) Access using GetDeriv Function
20 Local Response Function in MetrixND
21 Terminology Mapping Econometric y = f (X,ß) X s Y s Transformation Sample Coefficients Constant/Slope Estimation Iteration Recursive LS Errors Dynamic Neural Net Artificial Neural Network Network Inputs Network Outputs/Targets Hidden Layer Training Set Network Weights/Connections Bias/Tilt Training/Learning Epoch Back Propagation Mistakes Recurrent
22 Building Weather Splines
23 Capped and Uncapped Degree Days
24 Daily Weather Response Multi-part slopes using Capped Degree-Days HDD60_Cap5 = Min(Max 60 AvgDB, 0, 5) CDD60_Cap5 = Min(Max AvgDB 60, 0, 5)
25 Daily Weather Response Multi-part slopes using Capped Degree-Days HDD55_Cap5 = Min(Max 55 AvgDB, 0, 5) CDD65_Cap5 = Min(Max AvgDB 65, 0, 5)
26 Daily Weather Response Multi-part slopes using Capped Degree-Days HDD50_Cap5 = Min(Max 50 AvgDB, 0, 5) CDD70_Cap5 = Min(Max AvgDB 70, 0, 5)
27 Daily Weather Response Multi-part slopes using Capped Degree-Days HDD45_Cap5 = Min(Max 45 AvgDB, 0, 5) CDD75_Cap5 = Min(Max AvgDB 75, 0, 5)
28 Daily Weather Response Multi-part slopes using Capped Degree-Days HDD40_Cap5 = Min(Max 40 AvgDB, 0, 5) CDD80 = Max AvgDB 80, 0
29 Daily Weather Response Multi-part slopes using Capped Degree-Days HDD35 = Max 35 AvgDB, 0 CDD80 = Max AvgDB 80, 0
30 Daily Energy Vs Daily Average Temperature Multi-part slopes using Capped Degree Days
31 Derivative of NNet w.r.t. Temperature vs Daily Average Temperature Cooling Heating
32 Averaging the Slopes Maximum Powered CDD Average slope between 75 and GWh / Degree Average slope between 60 and GWh / Degree Cooling
33 Daily Weather Response CDDSpline = CDD60 Cap CDD65 Cap CDD70 Cap CDD75 Cap CDD80 Multi-part slopes using Capped Degree-Days
34 Averaging the Slopes Heating Maximum Powered CDD Average slope between 75 and GWh / Degree Average slope between 55 and GWh / Degree
35 Daily Weather Response Multi-part slopes using Capped Degree-Days HDDSpline = HDD60 Cap HDD55 Cap HDD50 Cap HDD45 Cap HDD40 Cap HDD35
36 Daily Weather Response CDDSpline = CDD60 Cap CDD65 Cap CDD70 Cap CDD75 Cap CDD80 Multi-part slopes using Capped Degree-Days HDDSpline = HDD60 Cap HDD55 Cap HDD50 Cap HDD45 Cap HDD40 Cap HDD35
37 Specification 2. Hourly Regression With Daily Weights
38 Specification 2: Daily DD Weights
39 Degree-day Variable Calculations (HR 17) Avg DB Daily Weights Hour by Hour DD Variables CDD HDD
40 Specification 2: Model Results
41 Hourly Weather Response
42 Hour 00 Load Vs Temperature
43 Hour 01 Load Vs Temperature
44 Hour 02 Load Vs Temperature
45 Hour 03 Load Vs Temperature
46 Hour 04 Load Vs Temperature
47 Hour 05 Load Vs Temperature
48 Hour 06 Load Vs Temperature
49 Hour 07 Load Vs Temperature
50 Hour 08 Load Vs Temperature
51 Hour 09 Load Vs Temperature
52 Hour 10 Load Vs Temperature
53 Hour 11 Load Vs Temperature
54 Hour 12 Load Vs Temperature
55 Hour 13 Load Vs Temperature
56 Hour 14 Load Vs Temperature
57 Hour 15 Load Vs Temperature
58 Hour 16 Load Vs Temperature
59 Hour 17 Load Vs Temperature
60 Hour 18 Load Vs Temperature
61 Hour 19 Load Vs Temperature
62 Hour 20 Load Vs Temperature
63 Hour 21 Load Vs Temperature
64 Hour 22 Load Vs Temperature
65 Hour 23 Load Vs Temperature
66 Specification 3. Hourly Neural Network
67 Specification 3. Hourly Neural Network Node 1 (Linear) Node 2 (Sigmoid) Node 3 (Sigmoid)
68 Specification 3: Model Results
69 Using Machine Learning To Determine The Hourly Weather Response
70 Hour 00 Derivative Vs Temperature
71 Hour 01 Derivative Vs Temperature Cooling Heating
72 Hour 02 Derivative Vs Temperature Cooling Heating
73 Hour 03 Derivative Vs Temperature Cooling Heating
74 Hour 04 Derivative Vs Temperature Cooling Heating
75 Hour 05 Derivative Vs Temperature Cooling Heating
76 Hour 06 Derivative Vs Temperature Cooling Heating
77 Hour 07 Derivative Vs Temperature Cooling Heating
78 Hour 08 Derivative Vs Temperature Cooling Heating
79 Hour 09 Derivative Vs Temperature Cooling Heating
80 Hour 10 Derivative Vs Temperature Cooling Heating
81 Hour 11 Derivative Vs Temperature Cooling Heating
82 Hour 12 Derivative Vs Temperature Cooling Heating
83 Hour 13 Derivative Vs Temperature Cooling Heating
84 Hour 14 Derivative Vs Temperature Cooling Heating
85 Hour 15 Derivative Vs Temperature Cooling Heating
86 Hour 16 Derivative Vs Temperature Cooling Heating
87 Hour 17 Derivative Vs Temperature Cooling Heating
88 Hour 18 Derivative Vs Temperature Cooling Heating
89 Hour 19 Derivative Vs Temperature Cooling Heating
90 Hour 20 Derivative Vs Temperature Cooling Heating
91 Hour 21 Derivative Vs Temperature Cooling Heating
92 Hour 22 Derivative Vs Temperature Cooling Heating
93 Hour 23 Derivative Vs Temperature Cooling Heating
94 Computing The Hourly Degree-Day Weights Average slope of 5 degree bucket = Max = Min = Slope Max/MinSlope
95 Specification 4. Hourly Regression With Hourly Weights
96 Specification 4: Model Results
97 Adding Rolling Lag DD Variables Specification 5 Add 6 Period Lag Specification 6 Add 24 Period Lag
98 Specification 5: Model Results
99 Specification 6: Model Results
100 Specification 7 Model Results
101 Specification 8: Model Results
102 Specification 9: Model Results
103 Specification 10: Results
104 Specification 11: Results
105 Conclusions Cooling Heating
106 Conclusions» Neural networks provide a powerful framework for identifying nonlinear relationships.» Detection and quantification of nonlinearities is a form of machine learning.» Neural network derivatives can be processed to construct weights for degree day splines.» Regression models using these splines are equivalent to the comparable neural networks.» Adding additional interaction variables results in regression models that are rich, accurate, and robust.
107 FORECASTING BLOG
108 2018 SCHEDULED EVENTS Workshops Dates Location Energy Forecasting 101 February 28 - March 1 Washington, DC Introduction to SAE April 24 Austin, TX Advanced Forecast Topics April 24 Austin, TX Fundamentals of Modeling Energy and Demand Forecasting October Chicago, IL Fundamentals of Short-term Operational Forecasting September San Diego, CA Meetings 16th Annual Energy Forecasting Meeting April Austin, TX 12th Annual ISO Forecasting Summit May San Diego, CA European User Meeting TBD TBD Itron Utility Week September 30-October 2 Scottsdale, AZ Webinars Using Neural Networks to Build Robust Hourly Models February 20 On-line Budget Forecasting A Practitioner s Handbook May 22 On-line 2018 Forecast Accuracy Benchmarking Survey and Energy Trends September 11 On-line Short-term Load Forecasting A Practitioner s Handbook December 4 On-line
109 CONTACT US
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