As included in Load Forecast Review Report (Page 1):
|
|
|
- Blaze Wade
- 5 years ago
- Views:
Transcription
1 As included in Load Forecast Review Report (Page 1): A key shortcoming of the approach taken by MH is the reliance on a forecast that has a probability of being accurate 50% of the time for a business with high capital costs and long project lead times, a forecast that is expected to address 90% of the potential futures is typically preferred. Revised Statement: A key shortcoming of the approach taken by MH is the reliance on a forecast that has a chance of the forecast being 50% higher or 50% lower than the actual load for a business with high capital costs and long project lead times, a forecast that is expected to address 90% of the potential futures is typically preferred Page 1 of 1
2 Page 32, Footnote 47 of Load Forecast Review Report: Price elasticity estimates the impact of a one percent change in electricity demand with a one percent change in electricity price. Revised Statement: Price elasticity estimates the impact of one percent change in electricity price on electricity demand Page 1 of 2
3 Page 46, Load Forecast Review Report: The weather adjusted annual gross firm energy is lower than actual gross firm energy since actual annual HDDs are lower than the normal HDD. Similarly, the weather adjusted load is greater than actual gross firm energy when the annual HDDs are higher than normal HDDs. Revised Statement: The weather adjusted annual gross firm energy is higher than actual gross firm energy since actual annual HDDs are lower than the normal HDD. Similarly, the weather adjusted load is lower than actual gross firm energy when the annual HDDs are higher than normal HDDs Page 1 of 3
4 Figure 12, Page 41, Load Forecast Review Report: Figure 12: Comparison of Historical Weather Adjusted Gross Firm Energy (GWh) with Multiple Forecast Vintages of Gross Firm Energy Page 1 of 4
5 Revised Figure 12: The Actual (2017 Weather Adjusted Energy) line includes annual cumulative programbased DSM savings 1 in addition to weather-adjusted actual load. Figure 12 Revised: Comparison of Historical Weather Adjusted Gross Firm Energy (GWh) with Multiple Forecast Vintages of Gross Firm Energy 1 The annual cumulative program-based DSM savings gathered from two excel workbooks shared with Daymark: (1) Excel file Res AveUse Model 2017_Daymark, Tab DSM 2017, Column G and (2) Excel file GS Mass Market May 1st_Daymark, Tab DSM 2017, Columns G and H Page 1 of 5
6 Figure 13, Page 42, Load Forecast Review Report: Figure 13: Comparison of Historical Weather Adjusted Residential Sales (GWh) with Multiple Forecast Vintages of Residential Sales Page 1 of 6
7 Revised Figure 13: The Actual (2017 Weather Adjusted Energy) line includes annual cumulative Residential program-based DSM savings 2 in addition to weather-adjusted actual load. Figure 13 Revised: Comparison of Historical Weather Adjusted Residential Sales (GWh) with Multiple Forecast Vintages of Residential Sales 2 The annual cumulative Residential program-based DSM savings gathered Excel file Res AveUse Model 2017_Daymark, Tab DSM 2017, Column G Page 1 of 7
8 Figure 14, Page 43, Load Forecast Review Report: Figure 14 Comparison of Historical Weather Adjusted General Service Mass Market Sales (GWh) with Multiple Forecast Vintages of GSMM Sales Page 1 of 8
9 Revised Figure 14: The Actual (2017 Weather Adjusted Energy) line for GSMM includes annual Commercial and Industrial cumulative program-based DSM savings 3 in addition to weather-adjusted actual load. Specifically, Actual (2017 Weather Adjusted Energy) line includes 100% of Commercial program-based DSM savings and 45% of Industrial program-based DSM savings to be consistent with how MH attributed Commercial and Industrial DSM savings in sector-level regression models. Moreover, for 2016/17, Actual (2017 Weather Adjusted Energy) line excludes load associated with seven Top Consumer customers (404 GWh) that were moved to GSMM category. Similarly, the forecast value for 2016/17 created during 2016 load forecast analysis also excludes this load. Figure 14 Revised: Comparison of Historical Weather Adjusted General Service Mass Market Sales (GWh) with Multiple Forecast Vintages of GSMM Sales 3 The annual cumulative program-based DSM savings gathered from Excel file GS Mass Market May 1st_Daymark, Tab DSM 2017, Columns G and H Page 1 of 9
10 Page 1 of 10
11 Figure 15, Page 44, Load Forecast Review Report: Figure 15: Comparison of Actual General Service Top Consumers Sales (GWh) with Multiple Forecast Vintages of Top Consumers Sales Page 1 of 11
12 Revised Figure 15: The Actual (2017 Weather Adjusted Energy) line for Top Consumers includes annual Industrial cumulative program-based DSM savings 4 in addition to actual sales. Specifically, Actual (2017 Weather Adjusted Energy) line includes 55% of Industrial program-based DSM savings to be consistent with how MH attributed Industrial DSM savings to Top Consumer category in PLIL regression methodology. Moreover, for 2016/17, Actual (2017 Weather Adjusted Energy) line includes load associated with seven Top Consumer customers (404 GWh) that were moved to GSMM category. Similarly, the forecast value for 2016/17 created during 2016 load forecast analysis also includes this load. Figure 15 Revised: Comparison of Actual General Service Top Consumers Sales (GWh) with Multiple Forecast Vintages of Top Consumers Sales 4 The annual cumulative program-based Industrial DSM savings gathered from Excel file GS Mass Market May 1st_Daymark, Tab DSM 2017, Column H Page 1 of 12
Page No. (and line no. if applicable):
COALITION/IEC (DAYMARK LOAD) - 1 COALITION/IEC (DAYMARK LOAD) 1 Tab and Daymark Load Forecast Page No. Page 3 Appendix: Review (and line no. if applicable): Topic: Price elasticity Sub Topic: Issue: Accuracy
LOADS, CUSTOMERS AND REVENUE
EB-00-0 Exhibit K Tab Schedule Page of 0 0 LOADS, CUSTOMERS AND REVENUE The purpose of this evidence is to present the Company s load, customer and distribution revenue forecast for the test year. The
BEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF COLORADO * * * * *
BEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF COLORADO * * * * * IN THE MATTER OF THE APPLICATION OF PUBLIC SERVICE COMPANY OF COLORADO FOR APPROVAL OF ITS 01 RENEWABLE ENERGY STANDARD COMPLIANCE
2013 WEATHER NORMALIZATION SURVEY. Industry Practices
2013 WEATHER NORMALIZATION SURVEY Industry Practices FORECASTING SPECIALIZATION Weather Operational Forecasting Short-term Forecasting to support: System Operations and Energy Trading Hourly Load Financial/Budget
BEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF COLORADO * * * *
Exhibit No. 1 BEFORE THE PUBLIC UTILITIES COMMISSION OF THE STATE OF COLORADO * * * * IN THE MATTER OF THE APPLICATION OF PUBLIC SERVICE COMPANY OF COLORADO FOR APPROVAL OF ITS 0 ELECTRIC RESOURCE PLAN
NSP Electric - Minnesota Annual Report Peak Demand and Annual Electric Consumption Forecast
Page 1 of 5 7610.0320 - Forecast Methodology NSP Electric - Minnesota Annual Report Peak Demand and Annual Electric Consumption Forecast OVERALL METHODOLOGICAL FRAMEWORK Xcel Energy prepared its forecast
SAN DIEGO GAS AND ELECTRIC COMPANY SOUTHERN CALIFORNIA GAS COMPANY 2013 TRIENNIAL COST ALLOCATION PROCEEDING (A ) (DATA REQUEST DRA-MPS-2)
QUESTION 1: Please provide the following in excel format for SCG and SDG&E: a) Provide historical quarterly data for all variables for the customer/meter forecast model (1988Ql to 2011Q4) workpapers page
CHAPTER 5 - QUEENSLAND FORECASTS
CHAPTER 5 - QUEENSLAND FORECASTS Summary This chapter presents information about annual energy, maximum demand (summer and winter), and nonscheduled generation for the Queensland region. It also includes
Multivariate Regression Model Results
Updated: August, 0 Page of Multivariate Regression Model Results 4 5 6 7 8 This exhibit provides the results of the load model forecast discussed in Schedule. Included is the forecast of short term system
Design of a Weather-Normalization Forecasting Model
Design of a Weather-Normalization Forecasting Model Final Briefing 09 May 2014 Sponsor: Northern Virginia Electric Cooperative Abram Gross Jedidiah Shirey Yafeng Peng OR-699 Agenda Background Problem Statement
Abram Gross Yafeng Peng Jedidiah Shirey
Abram Gross Yafeng Peng Jedidiah Shirey Contents Context Problem Statement Method of Analysis Forecasting Model Way Forward Earned Value NOVEC Background (1 of 2) Northern Virginia Electric Cooperative
PREPARED DIRECT TESTIMONY OF GREGORY TEPLOW SOUTHERN CALIFORNIA GAS COMPANY AND SAN DIEGO GAS & ELECTRIC COMPANY
Application No: A.1-0- Exhibit No.: Witness: Gregory Teplow Application of Southern California Gas Company (U 0 G) and San Diego Gas & Electric Company (U 0 G) for Authority to Revise their Natural Gas
Forecasting. Dr. Richard Jerz rjerz.com
Forecasting Dr. Richard Jerz 1 1 Learning Objectives Describe why forecasts are used and list the elements of a good forecast. Outline the steps in the forecasting process. Describe at least three qualitative
TRANSMISSION BUSINESS LOAD FORECAST AND METHODOLOGY
Filed: September, 00 EB-00-00 Tab Schedule Page of 0 TRANSMISSION BUSINESS LOAD FORECAST AND METHODOLOGY.0 INTRODUCTION 0 This exhibit discusses Hydro One Networks transmission system load forecast and
2013 Weather Normalization Survey. Itron, Inc El Camino Real San Diego, CA
Itron, Inc. 11236 El Camino Real San Diego, CA 92130 2650 858 724 2620 March 2014 Weather normalization is the process of reconstructing historical energy consumption assuming that normal weather occurred
FORECAST ACCURACY REPORT 2017 FOR THE 2016 NATIONAL ELECTRICITY FORECASTING REPORT
FORECAST ACCURACY REPORT 2017 FOR THE 2016 NATIONAL ELECTRICITY FORECASTING REPORT Published: November 2017 Purpose The National Electricity Rules (Rules) require AEMO to report to the Reliability Panel
CP:
Adeng Pustikaningsih, M.Si. Dosen Jurusan Pendidikan Akuntansi Fakultas Ekonomi Universitas Negeri Yogyakarta CP: 08 222 180 1695 Email : adengpustikaningsih@uny.ac.id Operations Management Forecasting
Regression Analysis Tutorial 77 LECTURE /DISCUSSION. Specification of the OLS Regression Model
Regression Analysis Tutorial 77 LECTURE /DISCUSSION Specification of the OLS Regression Model Regression Analysis Tutorial 78 The Specification The specification is the selection of explanatory variables
NATIONAL ELECTRICITY FORECASTING REPORT UPDATE FOR THE NATIONAL ELECTRICITY MARKET
NATIONAL ELECTRICITY FORECASTING REPORT UPDATE FOR THE NATIONAL ELECTRICITY MARKET Published: December 2014 IMPORTANT NOTICE Purpose The purpose of this publication is to report on the accuracy of the
Integrated Electricity Demand and Price Forecasting
Integrated Electricity Demand and Price Forecasting Create and Evaluate Forecasting Models The many interrelated factors which influence demand for electricity cannot be directly modeled by closed-form
Demand Forecasting Models
E 2017 PSE Integrated Resource Plan Demand Forecasting Models This appendix describes the econometric models used in creating the demand forecasts for PSE s 2017 IRP analysis. Contents 1. ELECTRIC BILLED
2013 FORECAST ACCURACY BENCHMARKING SURVEY AND ENERGY
2013 FORECAST ACCURACY BENCHMARKING SURVEY AND ENERGY Itron Forecasting Brown Bag June 4, 2013 Please Remember» Phones are Muted: In order to help this session run smoothly, your phones are muted.» Full
The Dayton Power and Light Company Load Profiling Methodology Revised 7/1/2017
The Dayton Power and Light Company Load Profiling Methodology Revised 7/1/2017 Overview of Methodology Dayton Power and Light (DP&L) load profiles will be used to estimate hourly loads for customers without
Gorge Area Demand Forecast. Prepared for: Green Mountain Power Corporation 163 Acorn Lane Colchester, Vermont Prepared by:
Exhibit Petitioners TGC-Supp-2 Gorge Area Demand Forecast Prepared for: Green Mountain Power Corporation 163 Acorn Lane Colchester, Vermont 05446 Prepared by: Itron, Inc. 20 Park Plaza, Suite 910 Boston,
REVISED UPDATED PREPARED DIRECT SAFETY ENHANCEMENT COST ALLOCATION TESTIMONY OF GARY LENART SAN DIEGO GAS & ELECTRIC COMPANY AND
Application No: Exhibit No.: Witness: A.--00 ) In the Matter of the Application of San Diego Gas & ) Electric Company (U 0 G) and Southern California ) Gas Company (U 0 G) for Authority to Revise ) Their
From Sales to Peak, Getting It Right Long-Term Demand Forecasting
From Sales to Peak, Getting It Right Long-Term Demand Forecasting 12 th Annual Energy Forecasters Meeting Las Vegas, NV April 2 April 3, 2014 Terry Baxter, NV Energy Manager, Forecasting Getting the Peak
Operations Management
3-1 Forecasting Operations Management William J. Stevenson 8 th edition 3-2 Forecasting CHAPTER 3 Forecasting McGraw-Hill/Irwin Operations Management, Eighth Edition, by William J. Stevenson Copyright
Introduction to Forecasting
Introduction to Forecasting Introduction to Forecasting Predicting the future Not an exact science but instead consists of a set of statistical tools and techniques that are supported by human judgment
Normalization of Peak Demand for an Electric Utility using PROC MODEL
Normalization of Peak Demand for an Electric Utility using PROC MODEL Mark Harris, Jeff Brown, and Mark Gilbert* Central and South West Services, Inc. Tulsa, Oklahoma Abstract This paper discusses the
2018 FORECAST ACCURACY BENCHMARKING SURVEY AND ENERGY TRENDS. Mark Quan
2018 FORECAST ACCURACY BENCHMARKING SURVEY AND ENERGY TRENDS Mark Quan Please Remember» Phones are Muted: In order to help this session run smoothly, your phones are muted.» Full Screen Mode: To make the
Use of Normals in Load Forecasting at National Grid
Use of Normals in Load Forecasting at National Grid Place your chosen image here. The four corners must just cover the arrow tips. For covers, the three pictures should be the same size and in a straight
Load Forecasting Technical Workshop. January 30, 2004
Load Forecasting Technical Workshop January 30, 2004 Agenda New Commercial Survey Results (20 Minutes) Changing Commercial Saturation s (20 Minutes) Conditional Demand Analysis (30 Minutes) Changing Commercial
BEFORE THE PUBLIC UTILITY COMMISSION OF THE STATE OF OREGON UE 294. Load Forecast PORTLAND GENERAL ELECTRIC COMPANY. Direct Testimony and Exhibits of
UE 294 / PGE / 1200 Dammen - Riter BEFORE THE PUBLIC UTILITY COMMISSION OF THE STATE OF OREGON UE 294 Load Forecast PORTLAND GENERAL ELECTRIC COMPANY Direct Testimony and Exhibits of Sarah Dammen Amber
Lecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University
Lecture 15 20 Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University Modeling for Time Series Forecasting Forecasting is a necessary input to planning, whether in business,
UNBILLED ESTIMATION. UNBILLED REVENUE is revenue which had been recognized but which has not been billed to the purchaser.
UNBILLED ESTIMATION UNBILLED REVENUE is revenue which had been recognized but which has not been billed to the purchaser. Presented By: Laura Ortega Sr. Manager Demand Side Analytics, CPS Energy & Andy
A tale of two cities. John Daley, CEO, Grattan Institute Work and life in cities: City strategy in Australia Melbourne Economic Forum 27 October 2016
A tale of two cities John Daley, CEO, Grattan Institute Work and life in cities: City strategy in Australia Melbourne Economic Forum 27 October 2016 A tale of two cities Shifts in consumption are driving
FINAL REPORT EVALUATION REVIEW OF TVA'S LOAD FORECAST RISK
Memorandum from the Office of the Inspector General Robert Irvin, WT 9C-K FINAL REPORT EVALUATION 2012-14507 REVIEW OF TVA'S LOAD FORECAST RISK As part of a series of reviews to evaluate the Tennessee
RD1 - Page 469 of 578
DOCKET NO. 45524 APPLICATION OF SOUTHWESTERN PUBLIC SERVICE COMPANY FOR AUTHORITY TO CHANGE RATES PUBLIC UTILITY COMMISSION OF TEXAS DIRECT TESTIMONY of JANNELL E. MARKS on behalf of SOUTHWESTERN PUBLIC
North Dakota Lignite Energy Industry s Contribution to the State Economy
Agricultural Economics Miscellaneous Report No. 186 May 2000 North Dakota Lignite Energy Industry s Contribution to the State Economy Randal C. Coon, and F. Larry Leistritz* Department of Agricultural
2014 FORECASTING BENCHMARK AND OUTLOOK SURVEY. Mark Quan and Stuart McMenamin September 16, 2014 Forecasting Brown Bag Seminar
2014 FORECASTING BENCHMARK AND OUTLOOK SURVEY Mark Quan and Stuart McMenamin September 16, 2014 Forecasting Brown Bag Seminar PLEASE REMEMBER» Phones are Muted: In order to help this session run smoothly,
Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa. Forecasting demand 02/06/03 page 1 of 34
demand -5-4 -3-2 -1 0 1 2 3 Dennis Bricker Dept of Mechanical & Industrial Engineering The University of Iowa Forecasting demand 02/06/03 page 1 of 34 Forecasting is very difficult. especially about the
peak half-hourly Tasmania
Forecasting long-term peak half-hourly electricity demand for Tasmania Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report for
peak half-hourly New South Wales
Forecasting long-term peak half-hourly electricity demand for New South Wales Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report
2006 IRP Technical Workshop Load Forecasting Tuesday, January 24, :00 am 3:30 pm (Pacific) Meeting Summary
2006 IRP Technical Workshop Load Forecasting Tuesday, January 24, 2006 9:00 am 3:30 pm (Pacific) Meeting Summary Idaho Oregon Utah Teri Carlock (IPUC) Ming Peng (OPUC), Bill Wordley (OPUC) Abdinasir Abdulle
SHORT TERM LOAD FORECASTING
Indian Institute of Technology Kanpur (IITK) and Indian Energy Exchange (IEX) are delighted to announce Training Program on "Power Procurement Strategy and Power Exchanges" 28-30 July, 2014 SHORT TERM
PROJECT ECONOMIC ANALYSIS
Electricity Transmission Expansion and Supply Improvement Project (RRP NEP 41155) PROJECT ECONOMIC ANALYSIS A. Overview 1. The economic analysis undertaken for the Electricity Transmission and Supply Improvement
Into Avista s Electricity Forecasts. Presented by Randy Barcus Avista Chief Economist Itron s Energy Forecaster s Group Meeting
Incorporating Global Warming Into Avista s Electricity Forecasts Presented by Randy Barcus Avista Chief Economist Itron s Energy Forecaster s Group Meeting May 1, 009 Las Vegas, Nevada Presentation Outline
Dynamic Macroeconomics: Problem Set 4
Dynamic Macroeconomics: Problem Set 4 Universität Siegen Dynamic Macroeconomics 1 / 28 1 Computing growth rates 2 Golden rule saving rate 3 Simulation of the Solow Model 4 Growth accounting Dynamic Macroeconomics
WEATHER NORMALIZATION METHODS AND ISSUES. Stuart McMenamin Mark Quan David Simons
WEATHER NORMALIZATION METHODS AND ISSUES Stuart McMenamin Mark Quan David Simons Itron Forecasting Brown Bag September 17, 2013 Please Remember» Phones are Muted: In order to help this session run smoothly,
SOCALGAS REBUTTAL TESTIMONY OF ROSE-MARIE PAYAN (CUSTOMERS) June 2015
Company: Southern California Gas Company (U0G) Proceeding: 01 General Rate Case Application: A.1--00 Exhibit: SCG-0 SOCALGAS REBUTTAL TESTIMONY OF ROSE-MARIE PAYAN (CUSTOMERS) June 01 BEFORE THE PUBLIC
TOP MARKET SURVEY INSTRUCTION SHEET. Requirements. Overview
INSTRUCTION SHEET TOP SURVEY TOP SURVEY INSTRUCTION SHEET Overview For nearly 40 years, the ACA has surveyed member agencies and conducted the Top Collection Market Survey. This survey provides critical
T E S L A T O A C Q U I R E S O L A R C I T Y
T E S L A T O A C Q U I R E S O L A R C I T Y C R E A T I N G T H E W O R L D S L E A D I N G S U S T A I N A B L E E N E R G Y C O M P A N Y I N V E S T O R P R E S E N T A T I O N A u g u s t 1, 2 0
Chapter 7 Forecasting Demand
Chapter 7 Forecasting Demand Aims of the Chapter After reading this chapter you should be able to do the following: discuss the role of forecasting in inventory management; review different approaches
Forecasting. Chapter Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall
Forecasting Chapter 15 15-1 Chapter Topics Forecasting Components Time Series Methods Forecast Accuracy Time Series Forecasting Using Excel Time Series Forecasting Using QM for Windows Regression Methods
Peak Load Forecasting
Peak Load Forecasting Eugene Feinberg Stony Brook University Advanced Energy 2009 Hauppauge, New York, November 18 Importance of Peak Load Forecasting Annual peak load forecasts are important for planning
Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall.
13 Forecasting PowerPoint Slides by Jeff Heyl For Operations Management, 9e by Krajewski/Ritzman/Malhotra 2010 Pearson Education 13 1 Forecasting Forecasts are critical inputs to business plans, annual
G200 & CL605 Analysis
G200 & CL605 Analysis Annual & Hourly Cost Detail Gulfstream G200 GENERAL PARAMETERS Min Crew / Max Passengers 2 / 8 Seats Full Range (NM / SM) 3051.75 / 3511.89 Normal Cruise Speed (KTS / MPH) 447.53
Operator s Forecast Report. Prepared by Alberta Energy for Oil Sands Royalty Business Training November 9, 2017
Operator s Forecast Report Prepared by Alberta Energy for Oil Sands Royalty Business Training November 9, 2017 Disclaimer: The information contained in this presentation is provided at the sole discretion
WP6 Early estimates of economic indicators. WP6 coordinator: Tomaž Špeh, SURS ESSNet Big data: BDES 2018 Sofia 14.,
WP6 Early estimates of economic indicators WP6 coordinator: Tomaž Špeh, SURS ESSNet Big data: BDES 2018 Sofia 14.,15.5.2018 WP6 objectives Outline Summary of activities carried out and results achieved
Lecture 4 Forecasting
King Saud University College of Computer & Information Sciences IS 466 Decision Support Systems Lecture 4 Forecasting Dr. Mourad YKHLEF The slides content is derived and adopted from many references Outline
APPENDIX 2 MATHEMATICAL EXPRESSION ON THE METHODOLOGY OF CONSTRUCTION OF INPUT OUTPUT TABLES
APPENDIX 2 MATHEMATICAL EXPRESSION ON THE METHODOLOGY OF CONSTRUCTION OF INPUT OUTPUT TABLES A2.1 This Appendix gives a brief discussion of methods of obtaining commodity x commodity table and industry
Annual & Hourly Cost Detail
Aircraft Analysis Annual & Hourly Cost Detail Embraer Phenom 100 GENERAL PARAMETERS Min Crew / Max Passengers 1 / 6 Seats Full Range (NM / SM) 896.00 / 1031.10 Normal Cruise Speed (KTS / MPH) 390.00 /
Building Inflation Tables and CER Libraries
Building Inflation Tables and CER Libraries January 2007 Presented by James K. Johnson Tecolote Research, Inc. Copyright Tecolote Research, Inc. September 2006 Abstract Building Inflation Tables and CER
Chris Doerr Phone: Aircraft Cost Calculator, LLC 1341 W. Mequon Road, Suite 205, Mequon, WI Powered by ACC:
Annual & Hourly Cost Detail Embraer Legacy 650 GENERAL PARAMETERS Min Crew / Max Passengers 2 / 13 Seats Full Range (NM / SM) 3569.48 / 4107.68 Normal Cruise Speed (KTS / MPH) 435.83 / 501.54 Average Pre-Owned
Chapter 3. Regression-Based Models for Developing Commercial Demand Characteristics Investigation
Chapter Regression-Based Models for Developing Commercial Demand Characteristics Investigation. Introduction Commercial area is another important area in terms of consume high electric energy in Japan.
Orange Visualization Tool (OVT) Manual
Orange Visualization Tool (OVT) Manual This manual describes the features of the tool and how to use it. 1. Contents of the OVT Once the OVT is open (the first time it may take some seconds), it should
Interstate Power & Light (IPL) 2013/2014
Page 1 of 8 I. Executive Summary MISO requires each load serving entity (LSE) to provide a forecast of peak at the time of the MISO peak. LSE ALTW is shared by Alliant Energy Interstate Power & Light (IPL)
Putting the U.S. Geospatial Services Industry On the Map
Putting the U.S. Geospatial Services Industry On the Map December 2012 Definition of geospatial services and the focus of this economic study Geospatial services Geospatial services industry Allow consumers,
LOAD POCKET MODELING. KEY WORDS Load Pocket Modeling, Load Forecasting
LOAD POCKET MODELING Eugene A. Feinberg, Dora Genethliou Department of Applied Mathematics and Statistics State University of New York at Stony Brook Stony Brook, NY 794-36, USA Janos T. Hajagos KeySpan
Report on System-Level Estimation of Demand Response Program Impact
Report on System-Level Estimation of Demand Response Program Impact System & Resource Planning Department New York Independent System Operator April 2012 1 2 Introduction This report provides the details
Demand and Supply Integration:
Demand and Supply Integration: The Key to World-Class Demand Forecasting Mark A. Moon FT Press Contents Preface xxi Chapter 1 Demand/Supply Integration 1 the Idea Behind DSI 2 How DSI Is Different from
Variables For Each Time Horizon
Variables For Each Time Horizon Andy Sukenik Itron s Forecasting Brown Bag Seminar December 13th, 2011 Please Remember In order to help this session run smoothly, your phones are muted. To make the presentation
Acetic Acid Industry Outlook in China to Market Size, Company Share, Price Trends, Capacity Forecasts of All Active and Planned Plants
Acetic Acid Industry Outlook in China to 2016 - Market Size, Company Share, Price Trends, Capacity Forecasts of All Active and Planned Plants Reference Code: GDCH1695IDB Publication Date: December 2012
Ameren Missouri Peak Load Forecast Energy Forecasting Meeting, Las Vegas. April 17-18, 2013
Ameren Missouri Peak Load Forecast Energy Forecasting Meeting, Las Vegas April 17-18, 2013 Motivation for End Use Peak Forecasting Missouri IRP rules have extremely detailed load analysis and forecasting
Train the model with a subset of the data. Test the model on the remaining data (the validation set) What data to choose for training vs. test?
Train the model with a subset of the data Test the model on the remaining data (the validation set) What data to choose for training vs. test? In a time-series dimension, it is natural to hold out the
Chapter 4. Explanation of the Model. Satoru Kumagai Inter-disciplinary Studies, IDE-JETRO, Japan
Chapter 4 Explanation of the Model Satoru Kumagai Inter-disciplinary Studies, IDE-JETRO, Japan Toshitaka Gokan Inter-disciplinary Studies, IDE-JETRO, Japan Ikumo Isono Bangkok Research Center, IDE-JETRO,
Antti Salonen PPU Le 2: Forecasting 1
- 2017 1 Forecasting Forecasts are critical inputs to business plans, annual plans, and budgets Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan: output
Functions of One Variable Basics
Functions of One Variable Basics Function Notation: = f() Variables and are placeholders for unknowns» We will cover functions of man variables during one of the in-residence QSW lectures. = independent
Design of a Weather- Normalization Forecasting Model
Design of a Weather- Normalization Forecasting Model Progress Report Abram Gross Yafeng Peng Jedidiah Shirey 3/4/2014 TABLE OF CONTENTS 1.0 Introduction... 3 2.0 Problem Statement... 3 3.0 Scope... 3 4.0
Defining Normal Weather for Energy and Peak Normalization
Itron White Paper Energy Forecasting Defining Normal Weather for Energy and Peak Normalization J. Stuart McMenamin, Ph.D Managing Director, Itron Forecasting 2008, Itron Inc. All rights reserved. 1 Introduction
E550 vs Mustang vs P Owner Hours/Annually
E550 vs Mustang vs P100 300 Owner Hours/Annually Annual & Hourly Cost Detail Eclipse 550 GENERAL PARAMETERS Min Crew / Max Passengers 1 / 6 Seats Full Range (NM / SM) 1125.00 / 1294.63 Normal Cruise Speed
WRF Webcast. Improving the Accuracy of Short-Term Water Demand Forecasts
No part of this presentation may be copied, reproduced, or otherwise utilized without permission. WRF Webcast Improving the Accuracy of Short-Term Water Demand Forecasts August 29, 2017 Presenters Maureen
PPU411 Antti Salonen. Forecasting. Forecasting PPU Forecasts are critical inputs to business plans, annual plans, and budgets
- 2017 1 Forecasting Forecasts are critical inputs to business plans, annual plans, and budgets Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan: output
Antti Salonen KPP Le 3: Forecasting KPP227
- 2015 1 Forecasting Forecasts are critical inputs to business plans, annual plans, and budgets Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan: output
Global and China Sodium Silicate Industry 2014 Market Research Report
2014 QY Research Reports Global and China Sodium Silicate Industry 2014 Market Research Report QY Research Reports included market size, share & analysis trends on Global and China Sodium Silicate Industry
The Use of EDD for Weather Normalisation
The Use of EDD for Weather Normalisation January 2014 Core Energy Group 2014 January 2014 i Disclaimer Disclaimer This document has been prepared by Core Energy Group Pty Limited, A.C.N. 110 347 085, holder
Difference in regional productivity and unbalance in regional growth
Difference in regional productivity and unbalance in regional growth Nino Javakhishvili-Larsen and Jie Zhang - CRT, Denmark, Presentation at 26 th International input-output conference in Brazil Aim of
Design of a Weather- Normalization Forecasting Model. Final Report
Design of a Weather- Normalization Forecasting Model Final Report Abram Gross Yafeng Peng Jedidiah Shirey 5/12/2014 Table of Contents 1.0 EXECUTIVE SUMMARY... 4 2.0 INTRODUCTION... 6 2.1 Background...
MACRO-PRUDENTIAL RULES AND REGULATION (2015): DISCLOSURE REQUIREMENTS ANNEXURE I: FORMAT FOR ANNUAL DISCLOSURES DRUK PNB BANK LTD.
MACRO-PRUDENTIAL RULES AND REGULATION (2015): DISCLOSURE REQUIREMENTS ANNEXURE I: FORMAT FOR ANNUAL DISCLOSURES DRUK PNB BANK LTD. For the Year ended December 31, 2016 Item 1: Tier I Capital and its Sub-components
Forecasting Chapter 3
Forecasting Chapter 3 Introduction Current factors and conditions Past experience in a similar situation 2 Accounting. New product/process cost estimates, profit projections, cash management. Finance.
peak half-hourly South Australia
Forecasting long-term peak half-hourly electricity demand for South Australia Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report
Global Coal Bed Methane (CBM) Market Professional Survey Report 2016
Published on Market Research Reports Inc. (https://www.marketresearchreports.com) Home > Global Coal Bed Methane (CBM) Market Professional Survey Report 2016 Global Coal Bed Methane (CBM) Market Professional
EC611--Managerial Economics
EC611--Managerial Economics Optimization Techniques and New Management Tools Dr. Savvas C Savvides, European University Cyprus Models and Data Model a framework based on simplifying assumptions it helps
Lecture Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University
Lecture 15 20 Prepared By: Mohammad Kamrul Arefin Lecturer, School of Business, North South University Modeling for Time Series Forecasting Forecasting is a necessary input to planning, whether in business,
"Natural" Cultural Districts and Neighborhood Revitalization
University of Pennsylvania ScholarlyCommons Culture and Community Revitalization: A Collaboration Social Impact of the Arts Project 6-2009 "Natural" Cultural Districts and Neighborhood Revitalization Mark
The TransPacific agreement A good thing for VietNam?
The TransPacific agreement A good thing for VietNam? Jean Louis Brillet, France For presentation at the LINK 2014 Conference New York, 22nd 24th October, 2014 Advertisement!!! The model uses EViews The
Ch. 12: Workload Forecasting
Ch. 12: Workload Forecasting Kenneth Mitchell School of Computing & Engineering, University of Missouri-Kansas City, Kansas City, MO 64110 Kenneth Mitchell, CS & EE dept., SCE, UMKC p. 1/2 Introduction
March 5, British Columbia Utilities Commission 6 th Floor, 900 Howe Street Vancouver, BC V6Z 2N3
Tom A. Loski Chief Regulatory Officer March 5, 2010 British Columbia Utilities Commission 6 th Floor, 900 Howe Street Vancouver, BC V6Z 2N3 16705 Fraser Highway Surrey, B.C. V4N 0E8 Tel: (604) 592-7464
Operations Management
Operations Management Chapter 4 Forecasting PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 7e Operations Management, 9e 2008 Prentice Hall, Inc. 4 1 Outline Global
2013 Weather Normalized MW and Preliminary 2014 ICAP Forecast
2013 Weather Normalized and Preliminary 2014 ICAP Forecast Arthur Maniaci System & Resource Planning New York Independent System Operator LFTF December 17, 2013 KCC 2013 New York Independent System Operator,