Modeling Electricity Demand: A Neural Network Approach Christian Crowley GWU Department of Economics 28 th Annual IAEE International Conference June 6,

Slides:



Advertisements
Similar presentations
Deep Learning Bing-Chen Tsai 1/21.
Advertisements

Authors: J.A. Hausman, M. Kinnucan, and D. McFadden Presented by: Jared Hayden.
SEKE 2014, Hyatt Regency, Vancouver, Canada
Hazırlayan NEURAL NETWORKS Least Squares Estimation PROF. DR. YUSUF OYSAL.
Discussion of “Is Realtime Pricing Green?” Benjamin F. Hobbs Yihsu Chen Dept. Geography & Environmental Engineering Whiting School of Engineering The Johns.
On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models Damien Fay, John V. Ringwood IEEE POWER SYSTEMS, 2010.
A neural network based several-hour- ahead electric load forecasting using similar days approach Paras Mandal, Tomonobu Senjyu, Naomitsu Urasaki, Toshihisa.
Neural Network Based Approach for Short-Term Load Forecasting
Dr. Yukun Bao School of Management, HUST Business Forecasting: Experiments and Case Studies.
Energy Calculations Dr. Sam C M Hui
Prénom Nom Document Analysis: Artificial Neural Networks Prof. Rolf Ingold, University of Fribourg Master course, spring semester 2008.
06/2000Short-term load forecasting1 SHORT TERM LOAD FORECASTING USING NEURAL NETWORKS AND FUZZY LOGIC George G Karady Arizona State University.
Chapter 3 Forecasting McGraw-Hill/Irwin
Load Forecasting Eugene Feinberg Applied Math & Statistics Stony Brook University NSF workshop, November 3-4, 2003.
MAE 552 Heuristic Optimization Instructor: John Eddy Lecture #30 4/15/02 Neural Networks.
Electrical Load Forecasting Using Machine Learning Techniques R. E. Abdel-Aal Center for Applied Physical Sciences (CAPS) Research Institute, KFUPM October.
Short-Term Load Forecasting In Electricity Market N. M. Pindoriya Ph. D. Student (EE) Acknowledge: Dr. S. N. Singh ( EE ) Dr. S. K. Singh ( IIM-L )
1 Econometric Load Forecasting Peak and Energy Forecast 06/14/2005 Econometric Load Forecasting Peak and Energy Forecast 06/14/2005.
Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Neural Networks Lab 5. What Is Neural Networks? Neural networks are composed of simple elements( Neurons) operating in parallel. Neural networks are composed.
2011 Long-Term Load Forecast Review ERCOT Calvin Opheim June 17, 2011.
2. Forecasting. Forecasting  Using past data to help us determine what we think will happen in the future  Things typically forecasted Demand for products.
Dan Simon Cleveland State University
System Planning & Operations Spring 2010
Production Planning and Control. 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 5. Linear regression Time-Series Models.
Stream-Based Electricity Load Forecast Authors: Joao Gama Pedro Pereira Rodrigues Presented by: Viktor Botev.
PM Model Performance Goals and Criteria James W. Boylan Georgia Department of Natural Resources - VISTAS National RPO Modeling Meeting Denver, CO May 26,
CCU Department of Electrical Engineering National Chung Cheng University, Taiwan Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch.
Northwest Power and Conservation Council 6 th Plan Conservation Resource Supply Curve Workshop on Data & Assumption Overview of Council Resource Analysis.
“Seasonality and Weather Effects on Electricity Loads” Crowley and Joutz, GWU Sept. 02 MetrixND Forecasters User Meeting September 23-25, 2002, San Diego,
Multiple-Layer Networks and Backpropagation Algorithms
Cascade Correlation Architecture and Learning Algorithm for Neural Networks.
Why Normal Matters AEIC Load Research Workshop Why Normal Matters By Tim Hennessy RLW Analytics, Inc. April 12, 2005.
© Copyright 2004 ECE, UM-Rolla. All rights reserved A Brief Overview of Neural Networks By Rohit Dua, Samuel A. Mulder, Steve E. Watkins, and Donald C.
Chapter 11 – Neural Networks COMP 540 4/17/2007 Derek Singer.
Backpropagation An efficient way to compute the gradient Hung-yi Lee.
Outline What Neural Networks are and why they are desirable Historical background Applications Strengths neural networks and advantages Status N.N and.
ESET ALEMU WEST Consultants, Inc. Bellevue, Washington.
Using Neural Networks to Predict Claim Duration in the Presence of Right Censoring and Covariates David Speights Senior Research Statistician HNC Insurance.
1 RECENT DEVELOPMENTS IN MULTILAYER PERCEPTRON NEURAL NETWORKS Walter H. Delashmit Lockheed Martin Missiles and Fire Control Dallas, TX 75265
Modeling Electricity Demand: A Neural Network Approach Christian Crowley GWU Department of Economics INFORMS Meeting October 26, 2004, Denver, CO.
Statistical Tools for Solar Resource Forecasting Vivek Vijay IIT Jodhpur Date: 16/12/2013.
May 03, UFE ANALYSIS Old – New Model Comparison Compiled by the Load Profiling Group ERCOT Energy Analysis & Aggregation May 03, 2007.
2016 Long-Term Load Forecast
V Bandi and R Lahdelma 1 Forecasting. V Bandi and R Lahdelma 2 Forecasting? Decision-making deals with future problems -Thus data describing future must.
ERCOT PUBLIC 10/7/ Load Forecasting Process Review Calvin Opheim Generation Adequacy Task Force October 7, 2013.
Hybrid Load Forecasting Method With Analysis of Temperature Sensitivities Authors: Kyung-Bin Song, Seong-Kwan Ha, Jung-Wook Park, Dong-Jin Kweon, Kyu-Ho.
On/Off Operation of Carbon Capture Systems in the Dynamic Electric Grid On/Off Operation of Carbon Capture Systems in the Dynamic Electric Grid Rochelle.
Professor : Ming – Shyan Wang Department of Electrical Engineering Southern Taiwan University Thesis progress report Sensorless Operation of PMSM Using.
Module 4 Forecasting Multiple Variables from their own Histories EC 827.
Methodology for Demand Forecasts for Bandera Electric Cooperative, Inc. Brian D. Bartos, P.E. Manager, Engineering Presented to ERCOT Reliability & Operations.
Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and l Chapter 14 l Time Series: Understanding Changes over Time.
CHEE825 Fall 2005J. McLellan1 Nonlinear Empirical Models.
EGU Forecasting Tool (Data Elements) Office of Air Quality (800) John Welch Senior Environmental Manager IDEM.
PPT 3 -1 Don R. Hansen Maryanne M. Mowen COST MANAGEMENT.
Vermont Power Supply September 30, 2005 Dave Lamont Vermont Department of Public Service.
Computacion Inteligente Least-Square Methods for System Identification.
Operation and Control Strategy of PV/WTG/EU Hybrid Electric Power System Using Neural Networks Faculty of Engineering, Elminia University, Elminia, Egypt.
LOAD FORECASTING. - ELECTRICAL LOAD FORECASTING IS THE ESTIMATION FOR FUTURE LOAD BY AN INDUSTRY OR UTILITY COMPANY - IT HAS MANY APPLICATIONS INCLUDING.
Multi-Area Load Forecasting for System with Large Geographical Area S. Fan, K. Methaprayoon, W. J. Lee Industrial and Commercial Power Systems Technical.
MAPE Publication Neil McAndrews For Bob Ryan of Deutsche Bank.
Chapter 11 – Neural Nets © Galit Shmueli and Peter Bruce 2010 Data Mining for Business Intelligence Shmueli, Patel & Bruce.
Data Mining: Concepts and Techniques1 Prediction Prediction vs. classification Classification predicts categorical class label Prediction predicts continuous-valued.
BUS 308 Week 4 Quiz Check this A+ tutorial guideline at 1. With reference to problem 1, what.
Deep Feedforward Networks
Wave height prediction in the Apostle Islands
Luís Filipe Martinsª, Fernando Netoª,b. 
Behavior Modification Report with Peak Reduction Component
Using Industrial Operating Models for Cost Benefit Analysis
Time Series Forecasting with Recurrent Neural Networks NN3 Competition Mahmoud Abou-Nasr Research & Advanced Engineering Ford Motor Company
Presentation transcript:

Modeling Electricity Demand: A Neural Network Approach Christian Crowley GWU Department of Economics 28 th Annual IAEE International Conference June 6, 2005, Taipei, Taiwan

“ANN Electricity Modeling.” Crowley, GWU. Support Co-researchers in the project are Frederick Joutz from the George Washington University Department of Economics Benjamin Hobbs and Yihsu Chen from the Johns Hopkins University Department of Geography and Environmental Engineering Hugh Ellis from the Johns Hopkins University School of Public Health This study is part of a larger joint effort supported by a EPA STAR grant: “Implications of Climate Change for Regional Air Pollution and Health Effects and Energy Consumption Behavior”

“ANN Electricity Modeling.” Crowley, GWU. Outline I.Context of the Research II.Introduction to ANN Modeling III.Basics of Electricity Demand IV.Developing the Demand Model V.Results

“ANN Electricity Modeling.” Crowley, GWU. I.Context of the Research

“ANN Electricity Modeling.” Crowley, GWU. Context The modeling efforts of the STAR grant are 1.Electricity load modeling and forecasting 2. … 3. … 4. …

“ANN Electricity Modeling.” Crowley, GWU. Context The modeling efforts of the STAR grant are 1.Electricity load modeling and forecasting 2.Electricity generation and dispatch modeling 3. … 4. …

“ANN Electricity Modeling.” Crowley, GWU. Context The modeling efforts of the STAR grant are 1.Electricity load modeling and forecasting 2.Electricity generation and dispatch modeling 3.Regional air pollution modeling 4. …

“ANN Electricity Modeling.” Crowley, GWU. Context The modeling efforts of the STAR grant are 1.Electricity load modeling and forecasting 2.Electricity generation and dispatch modeling 3.Regional air pollution modeling 4.Health effects characterization

“ANN Electricity Modeling.” Crowley, GWU. II.Intro to ANN Modeling

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Independent Variables Dependent Variables ANN

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Input Layer Output Layer

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Elements of a neuron

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Elements of a neuron Multiplicative Weight (w)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Elements of a neuron Multiplicative Weight (w) Additive Bias (b)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Elements of a neuron Multiplicative Weight (w) Additive Bias (b) Transfer Function ( f )

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Elements of a neuron Multiplicative Weight (w) Additive Bias (b) Transfer Function ( f ) The Neuron uses these elements in its operation, when it receives an input (p) and produces a result (a)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Once a neuron receives an input (p), the neuron’s operation consists of two parts:

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Once a neuron receives an input (p), the neuron’s operation consists of two parts: Linear Transformation using the weight (w) and bias (b)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Once a neuron receives an input (p), the neuron’s operation consists of two parts: Linear Transformation using the weight (w) and bias (b) Application of the Transfer Function ( f )

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Once a neuron receives an input (p), the neuron’s operation consists of two parts: Linear Transformation using the weight (w) and bias (b) Application of the Transfer Function ( f ) The neuron then passes the result (a) on to the next part of the ANN.

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Linear Transformation Multiply the input (p) by a weight (w) and add the bias (b) to get the intermediate result (n):

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Linear Transformation Multiply the input (p) by a weight (w) and add the bias (b) to get the intermediate result (n): n = w x p + b

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Linear Transformation Multiply the input (p) by a weight (w) and add the bias (b) to get the intermediate result (n): n = w x p + b Pass the intermediate result (n) to the transfer function

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Transfer Function Apply the transfer function ( f ) to the intermediate result (n) to create the neuron’s result (a)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Transfer Function Apply the transfer function ( f ) to the intermediate result (n) to create the neuron’s result (a) For neurons in the input layer, the transfer function is typically a hard-limiting switch at some threshold, say n = 0: f(n) = (0 for n ≤ 0; 1 for n > 0)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Transfer Function Apply the transfer function ( f ) to the intermediate result (n) to create the neuron’s result (a) For neurons in the input layer, the transfer function is typically a hard-limiting switch at some threshold, say n = 0: f(n) = (0 for n ≤ 0; 1 for n > 0) For neurons in the output layer, the “pure linear” transfer function typically has no effect: f(n) = n

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Example: A Neuron’s Operation Say a neuron has a weight of 10, a bias of 100, and a hard-limiting transfer function with a threshold of n = 0: w = 10; b = 100 f(n) = (0 for n ≤ 0; 1 for n > 0)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Example: A Neuron’s Operation Say a neuron has a weight of 10, a bias of 100, and a hard-limiting transfer function with a threshold of n = 0: w = 10; b = 100 f(n) = (0 for n ≤ 0; 1 for n > 0) Say the neuron receives an input of 5: p = 5

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Example: A Neuron’s Operation Say a neuron has a weight of 10, a bias of 100, and a hard-limiting transfer function with a threshold of n = 0: w = 10; b = 100 f(n) = (0 for n ≤ 0; 1 for n > 0) Say the neuron receives an input of 5: p = 5 The neuron’s linear transformation produces an intermediate result of 150: n = 10 x = 150

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture Example: A Neuron’s Operation Say a neuron has a weight of 10, a bias of 100, and a hard-limiting transfer function with a threshold of n = 0: w = 10; b = 100 f(n) = (0 for n ≤ 0; 1 for n > 0) Say the neuron receives an input of 5: p = 5 The neuron’s linear transformation produces an intermediate result of 150: n = 10 x = 150 The neuron’s transfer function produces the neuron’s result of 1: a = f(n) = 1

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Architecture

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Selection The weights and biases are determined by the ANN during training

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Selection The weights and biases are determined by the ANN during training Training involves presenting the ANN with input-output examples

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Selection The weights and biases are determined by the ANN during training Training involves presenting the ANN with input-output examples Training is finding the weights and biases that minimize the performance function

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Selection The weights and biases are determined by the ANN during training Training involves presenting the ANN with input-output examples Training is finding the weights and biases that minimize the performance function The performance function is typically some measure of model error, such as MSE

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Selection To build an ANN, the researcher specifies  the number of layers Input Output Hidden (if any)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Selection To build an ANN, the researcher specifies  the number of layers Input Output Hidden (if any)  the number of neurons in each layer typically 1-5 input neurons typically 1 output neuron

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Selection To build an ANN, the researcher specifies  the number of layers Input Output Hidden (if any)  the number of neurons in each layer input layer: typically 1-5 neurons output layer: typically 1 neuron  the type of transfer function for each neuron input layer: typically hard-limiting (0-1 switch) output layer: typically pure-linear (no effect)

“ANN Electricity Modeling.” Crowley, GWU. ANN Model Selection The researcher specifies  the number of layers Input Output Hidden (if any)  the number of neurons in each layer input layer: typically 1-5 neurons output layer: typically 1 neuron  the type of transfer function for each neuron input layer: typically hard-limiting (0-1 switch) output layer: typically pure-linear (no effect)  the performance function for the network (MSE)

“ANN Electricity Modeling.” Crowley, GWU. III.Basics of Electricity Demand

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Utilities are interested in forecasting peak demand

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Utilities are interested in forecasting peak demand Peak demand determines how many generators the utility must bring on line

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Utilities are interested in forecasting peak demand Peak demand determines how many generators the utility must bring on line Overestimating demand is costly

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Utilities are interested in forecasting peak demand Peak demand determines how many generators the utility must bring on line Overestimating demand is costly, wasteful Underestimating peak leads to electricity shortfalls

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Hourly peak electricity demand depends on Weather …

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Hourly peak electricity demand depends on Weather Time of the Year …

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Hourly peak electricity demand depends on Weather Time of the Year Day of the Week …

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Hourly peak electricity demand depends on Weather Time of the Year Day of the Week Holidays …

“ANN Electricity Modeling.” Crowley, GWU. Electricity Demand Hourly peak electricity demand depends on Weather Time of the Year Day of the Week Holidays Year Recent Electricity Demand

“ANN Electricity Modeling.” Crowley, GWU. IV.Developing the Demand Model

“ANN Electricity Modeling.” Crowley, GWU. General Model Independent Variable (Peak Electric Demand in MWh):

“ANN Electricity Modeling.” Crowley, GWU. General Model Independent Variable (Peak Electric Demand in MWh): h = 1…24 is the hour being modeled

“ANN Electricity Modeling.” Crowley, GWU. General Model Independent Variable (Peak Electric Demand in MWh): h = 1…24 is the hour being modeled d = January 1, 1995 … September 30, 2003 is the date of the observation

“ANN Electricity Modeling.” Crowley, GWU. General Model Dependent Variables 1. Weather (Temperature in Fahrenheit degrees): h = 1…24 is the hour being modeled d = January 1, 1995 … September 30, 2003 is the date of the observation

“ANN Electricity Modeling.” Crowley, GWU. General Model Dependent Variables 2. Time of the Year: i = 1…12 is the month of the observation (Month 1 = 1 for January, 0 otherwise, Month 2 = 1 for February, 0 otherwise, etc.)

“ANN Electricity Modeling.” Crowley, GWU. General Model Dependent Variables 3. Day of the Week: Day j j = 1…7 is the weekday of the observation (Day 1 = 1 for Monday, 0 otherwise, Day 2 = 1 for Tuesday, 0 otherwise, etc.)

“ANN Electricity Modeling.” Crowley, GWU. General Model Dependent Variables 4. Holidays: Holiday dummy for recording days with different demand profiles due to businesses closing (Holiday = 1 for New Years Day, Independence Day etc., and 0 otherwise)

“ANN Electricity Modeling.” Crowley, GWU. General Model Dependent Variables 5. Year of observation: Year records the year of the observation to account for trends

“ANN Electricity Modeling.” Crowley, GWU. General Model Dependent Variables 6. Recent Electricity Demand: electricity demand for the previous three hours

“ANN Electricity Modeling.” Crowley, GWU. General Model Dependent Variables 5. Recent Electricity Demand: electricity demand for the previous three hours electricity demand for the previous day at this hour

“ANN Electricity Modeling.” Crowley, GWU. General Model

“ANN Electricity Modeling.” Crowley, GWU. V.Model Selection

“ANN Electricity Modeling.” Crowley, GWU. Data Electricity demand data were obtained from PJM, a large Independent System Operator (ISO) in the mid-Atlantic U.S.

“ANN Electricity Modeling.” Crowley, GWU. Data Electricity demand data were obtained from PJM, a large Independent System Operator (ISO) in the mid-Atlantic U.S. Temperature data were obtained from the US National Climatic Data Center (NCDC) for the dates and areas of interest

“ANN Electricity Modeling.” Crowley, GWU. Model Selection  I chose to build an ANN with two layers Input Layer with 1-20 hard-limiting neurons Output Layer with 1 pure-linear neuron

“ANN Electricity Modeling.” Crowley, GWU. Model Selection  I chose to build an ANN with two layers Input Layer with 1-20 hard-limiting neurons Output Layer with 1 pure-linear neuron  I trained the ANN using the MSE as the performance function

“ANN Electricity Modeling.” Crowley, GWU. Model Selection  I chose to build an ANN with two layers Input Layer with 1-20 hard-limiting neurons Output Layer with 1 pure-linear neuron  I trained the ANN using the MSE as the performance function  The optimal number of input neurons was to be determined by evaluating their effect on the ANN

“ANN Electricity Modeling.” Crowley, GWU. Model Selection Adding neurons allows a model to be more flexible

“ANN Electricity Modeling.” Crowley, GWU. Model Selection Adding neurons allows a model to be more flexible But each additional neuron requires estimating several more parameters (weight and bias vectors)

“ANN Electricity Modeling.” Crowley, GWU. Model Selection Adding neurons allows a model to be more flexible But each additional neuron requires estimating several more parameters (weight and bias vectors) McMenamin and Monforte (1998) suggest observing the Schwartz Information Criterion (SIC) as additional neurons are added to an ANN

“ANN Electricity Modeling.” Crowley, GWU. Model Selection The SIC is a measure of model performance, based on the MSE, penalized for degrees of freedom. (NB the statistic reported is often the natural log of the SIC)

“ANN Electricity Modeling.” Crowley, GWU. Model Selection The SIC is a measure of model performance, based on the MSE, penalized for degrees of freedom. (NB the statistic reported is often the natural log of the SIC) If a neuron’s benefit outweighs its cost, SIC falls

“ANN Electricity Modeling.” Crowley, GWU. Model Selection The SIC is a measure of model performance, based on the MSE, penalized for degrees of freedom. (NB the statistic reported is often the natural log of the SIC) If a neuron’s benefit outweighs its cost, SIC falls When the SIC starts to rise, the optimal number of neurons has likely been surpassed

“ANN Electricity Modeling.” Crowley, GWU. Model Selection SIC as a Function of Neurons in the Input Layer Hour 13 – Hour 18

“ANN Electricity Modeling.” Crowley, GWU. Model Selection SIC as a Function of Neurons in the Input Layer Average over all 24 Hours

“ANN Electricity Modeling.” Crowley, GWU. V.Model Results

“ANN Electricity Modeling.” Crowley, GWU. Model Results For hourly models estimated for the Summer months: four models use 1 input neurons fifteen models use 2 input neurons four models use 3 input neurons one model uses 4 input neurons

“ANN Electricity Modeling.” Crowley, GWU. Model Results ANN models typically perform well with abundant data from non-linear relationships. The hourly models account for about 99% of the variation in the test data, with MAPE around 1.2%.

“ANN Electricity Modeling.” Crowley, GWU. Model Results The models appear to be biased towards low values in the test period

“ANN Electricity Modeling.” Crowley, GWU. Model Results The models appear to be biased towards low values in the test period As the test period is composed of the most recent data, this bias may be due to a growth trend (data were not de-trended)

“ANN Electricity Modeling.” Crowley, GWU. Model Results The models appear to be biased towards low values in the test period As the test period is composed of the most recent data, this bias may be due to a growth trend (data were not de-trended) Bias could also be due to high demand in the test period, or a structural shift

“ANN Electricity Modeling.” Crowley, GWU. Model Results Typical Performer – Hour 12

“ANN Electricity Modeling.” Crowley, GWU. Model Results Poor Performer – Hour 14