Monday, July 9, 2018

From Club Convergence of Per Capita Industrial Pollutant Emissions to Industrial Transfer Effects: An Empirical Study Across 285 Cities in China

China has grown to the world's second largest economy by nominal GDP. Many factors attribute to such rapid growth, such as globalization and hard-working Chinese people. Nevertheless, we can't ignore the pollution resulted from the industrialization. Dr. Chang Liu brought the research problem to me when she visited BigDEAL last year. We spent a year investigating the relationship between industrial transfer effects and per capita industrial pollutant emissions across 285 cities in China. We identified four convergence clubs for SO2 emissions, and three convergence clubs for soot emissions. We also concluded that industrial transfer effects can lead to multiple steady-state equilibria. This presents some evidence to support region-specific environmental policies and execution strategies. 

This is the first time I sent a paper to Energy Policy. The original version was submitted on Feb 5, 2018. Within five months, the paper was published after three revisions. The entire publication process was quite pleasant.

Citation
Chang Liu, Tao Hong, Huaifeng Liu, and Lili Wang, "From club convergence of per capita industrial pollutant emissions to industrial transfer effects: an empirical study across 285 cities in China," Energy Policy, vol.121, pp 300-313, October 2018. (ScienceDirect)

From Club Convergence of Per Capita Industrial Pollutant Emissions to Industrial Transfer Effects: An Empirical Study Across 285 Cities in China

Chang Liu, Tao Hong, Huaifeng Liu, and Lili Wang

Abstract

The process of industrialization has led to an increase in air pollutant emissions in China. At the regional level, industrial restructuring and industrial transfer from eastern China to western China have caused a significant difference in pollutant emissions among various cities. This paper analyzes per capita industrial pollutant emissions across 285 prefecture-level cities from 2003 to 2015, aiming to reveal how industrial transfer affects the formation of convergence clubs. Whether industrial pollutant emissions across heterogeneous cities converge to a unique steady-state equilibrium is first identified based on the concept of club convergence. Logit regression analysis is then applied to assess the effects of industrial transfer on the observed clubs. The log t-test highlights four convergence clubs for industrial SO2 emissions and three clubs for industrial soot emissions. The regression analysis results reveal that the effects of industrial transfer can lead to multiple steady-state equilibria, suggesting region-specific environmental policies and execution strategies. In addition, accelerating the development of clean energy technologies in emission-intense regions should be further emphasized. 

Monday, June 25, 2018

Big Data Analytics: Making Smart Grid Smarter

The May 2018 issue of the Power & Energy Magazine is on Big Data Analytics. My guest editorial is on IEEE Xplore with open access. The original articles are in English. The Spanish translation is also available. The links to these articles are listed below.

Citation

Tao Hong, "Big data analytics: making smart grid smarter" IEEE Power and Energy Magazine, vol.16, no.3, pp 12-16, May-June 2018. (IEEE Xplore)

Features in This Issue

Visualizing Big Energy Data
By Rob J. Hyndman, Xueqin (Amy) Liu, and Pierre Pinson

Distribution Synchrophasors
By Hamed Mohsenian-Rad, Emma Stewart, and Ed Cortez

Big Data Analytics for Flexible Energy Sharing
By Furong Li, Ran Li, Zhipeng Zhang, Mark Dale, David Tolley, and Petri Ahokangas

Weather Data for Energy Analytics
By Jonathan Black, Alex Hofmann, Tao Hong, Joseph Roberts, and Pu Wang

Big Data Analytics in China’s Electric Power Industry
By Chongqing Kang, Yi Wang, Yusheng Xue, Gang Mu, and Ruijin Liao

Training Energy Data Scientists
By Tao Hong, David Wenzhong Gao, Tom Laing, Dale Kruchten, and Jorge Calzada


Articulos de Mayo/Junio de 2018

Visualización de "big data" de energía
Por Rob J. Hyndman, Xueqin (Amy) Liu y Pierre Pinson

Sincrofasores en la distribución
Por Hamed Mohsenian-Rad, Emma Stewart y Ed Cortez

Análisis de "big data" para el intercambio flexible de energía
Por Furong Li, Ran Li, Zhipeng Zhang, Mark Dale, David Tolley y Petri Ahokangas

Datos meteorológicos para el análisis de energía
Por Jonathan Black, Alex Hofmann, Tao Hong, Joseph Robert y Pu Wang

Análisis de "big data" en la industria de la potencia eléctrica de china
Por Chongqing Kang, Yi Wang, Yusheng Xue, Gang Mu y Ruijin Liao

Formación de científicos de datos de energía
Por Tao Hong, David Wenzhong Gao, Tom Laing, Dale Kruchten y Jorge Calzada

Saturday, June 23, 2018

Call For Papers: Food and Agriculture Forecasting | International Journal of Forecasting

International Journal of Forecasting

Special Section on Food and Agriculture Forecasting

The fast growing world population brings a critical challenge to humanity: how to ensure adequate supply and access to safe, healthy food. Accurate forecasts provide valuable information to help in formulating national food and agricultural policies, and to help agriculture companies and farmers adjust their business strategies. Such forecasts cover production, consumption, stocks, trade and prices of major field crops (e.g., corn, sorghum, barley, oats, wheat, rice, soybeans, and cotton) and livestock (e.g., beef, pork, poultry and eggs, and dairy). This special section is to collect high-quality research that involves theoretical and practical aspects of forecasting in food and agriculture. Specifically, it encourages papers that inspire actionable insights and/or make methodological breakthroughs in this area.

Potential topics include but are not limited to:

  • Forecasting methodologies in food and agriculture
  • Major field crops forecasting
  • Livestock forecasting 
  • Agri-food products forecasting 
  • Forecasting in vegetables, fruits and other agriculture commodities
  • Agriculture commodities futures market forecasting
  • Natural resources forecasting in agriculture and food industry 
  • Water and energy forecasting in agriculture 
  • Climate forecasting in agriculture

Submission deadline: 31 December 2018

To submit a paper for consideration for the Special Section, please upload your paper online and include a cover letter clearly indicating that the paper is for the special issue “Food and Agriculture Forecasting”. The webpage for online submission is mc.manuscriptcentral.com/ijf. Instructions for authors are provided at www.forecasters.org/ijf/authors. All papers will follow IJF’s double-blind refereeing process. For further information about the Special Section, please contact the guest editors.

Guest Editors

Jue Wang, Chinese Academy of Sciences, China (wjue@amss.ac.cn)
Tao Hong, University of North Carolina at Charlotte, USA (hong@uncc.edu)

Monday, June 18, 2018

Combining Probabilistic Load Forecasts

We often find simple averaging as a plausible solution for combining point forecasts. Combining probabilistic forecasts is not that trivial. The literature of combining probabilistic load forecasts is rather limited. Previously, we developed a Quantile Regression Averaging (QRA) method to generate probabilistic load forecasts by combining point forecasts. This work is a follow up, where we combine probabilistic load forecasts to generate a more accurate probabilistic forecast. The method we proposed here is a Constrained Quantile Regression Averaging (CQRA) method, where the parameters of a quantile regression model are non-negative and sum up to 1. We applied the method to loads at both high voltage level and household level, showing better results than the benchmarks.

Among my papers published so far, this one has the shortest title.

Citation
Yi Wang, Ning Zhang, Yushi Tan, Tao Hong, Daniel Kirschen, and Chongqing Kang, "Combining probabilistic load forecasts," IEEE Transactions on Smart Grid, in press, available online. (arXiv; IEEE Xplore).

Combining Probabilistic Load Forecasts

Yi Wang, Ning Zhang, Yushi Tan, Tao Hong, Daniel Kirschen, and Chongqing Kang

Abstract

Probabilistic load forecasts provide comprehensive information about future load uncertainties. In recent years, many methodologies and techniques have been proposed for probabilistic load forecasting. Forecast combination, a widely recognized best practice in point forecasting literature, has never been formally adopted to combine probabilistic load forecasts. This paper proposes a constrained quantile regression averaging (CQRA) method to create an improved ensemble from several individual probabilistic forecasts. We formulate the CQRA parameter estimation problem as a linear program with the objective of minimizing the pinball loss and the constraints that the parameters are nonnegative and summing up to one. We demonstrate the effectiveness of the proposed method using two publicly available datasets, the ISO New England data and Irish smart meter data. Comparing with the best individual probabilistic forecast, the ensemble can reduce the pinball score by 4.39% on average. The proposed ensemble also demonstrates superior performance over nine other benchmark ensembles.

Thursday, June 14, 2018

A Semi-heterogeneous Approach to Combining Crude Oil Price Forecasts

Forecast combination is an effective method to enhance the accuracy. Most combination methods in the literature can be grouped two categories, heterogeneous combination and homogeneous combination, with each having pros and cons. I collaborated with my former visiting scholar Dr. Jue Wang and her colleagues to develop a semi-heterogeneous approach to combining forecasts. We leveraged the decomposition-reconstruction concept, mixing and matching 4 decomposition methods with 4 forecasting techniques. In total this process generates 16 forecasts for combination, which is easier than applying 16 completely different techniques (a.k.a. heterogeneous combination) and more robust than producing 16 different forecasts from one technique (a.k.a. homogeneous combination). Furthermore, the proposed method leads to more accurate forecasts than its counterparts.

Citation
Jue Wang, Xiang Li, Tao Hong, and Shouyang Wang, "A semi-heterogeneous approach to combining crude oil price forecasts," Information Sciences, vol.460-461, pp 279-292, September 2018. (ScienceDirect)


A Semi-heterogeneous Approach to Combining Crude Oil Price Forecasts

Jue Wang, Xiang Li, Tao Hong, and Shouyang Wang

Abstract

Crude oil price forecasting has received increased attentions due to its significant role in the global economy. Accurate crude oil price forecasts often lead to a rapid new production development with higher quality and less cost. Making such accurate forecasts, however, is challenging due to the intrinsic complexity of oil market mechanism. Many techniques have been tested in the crude oil price forecasting literature. Although forecast combination is a well-known method to improve forecast accuracy, generating forecasts using various techniques tend to be labor intensive. How to efficiently generate many individual forecasts for combination becomes a research question in crude oil price forecasting. Recently, several signal decomposition methods have been suggested for processing the oil price signals. In this paper, we propose a semi-heterogeneous approach to combining crude oil price forecasts, which interacts a set of decomposition methods with a set of forecasting techniques. We first decompose the original price series using four decomposition methods, such as Wavelet Analysis, Singular Spectral Analysis, Empirical Mode Decomposition, and Variational Mode Decomposition. We then use four different forecasting techniques, such as Autoregressive Models, Autoregressive Integrated Moving Average Models, Artificial Neural Networks, and Support Vector Regression Models, to forecast the components from each decomposition methods. Finally, we reconstruct the price forecasts from the forecasted components. This process generates 16 price forecasts in total for combination. We test the combination based on all individual forecasts, as well as a subset of the individual forecasts selected using Tabu Search. The experimental results demonstrate that the forecasting models with the addition of a decomposition technique can have an error reduction of 30.6% compared to benchmark models on average. The combined forecasts outperform the individual forecasts on average. Furthermore, comparing with the heterogeneous combination of 4 individual forecasts, the semi-heterogeneous combinations reduce the errors by 56.6% (w/o Tabu Search) and 61.6% (w/ Tabu Search).

Friday, April 27, 2018

Weather Data for Energy Analytics

Being an energy forecaster, I am genuinely interested in meteorology. I even recruited a master student who was a practicing meteorologist in Hawaii (see the blog post about Ying Chen). The more energy forecasting projects I conduct, the more I appreciate the value of weather data. In GEFCom2014, the top 1 place of the solar track was a team of meteorologists from Australia, who completely dominated the track. In GEFCom2017, the top 1 place of the final match was a team of meteorologists from Japan. I truly believe that the energy forecasting community can better leverage meteorology than what we do today. Here is an article about two use cases of weather data for energy analytics. In fact we merged two papers into one by removing the sophisticated mathematics and statistics to keep the story readable to a broad audience. The IEEE Power and Energy Society is so kind to offer the open access to this paper, so that people can read it for free.

Citation

Jonathan Black, Alex Hofmann, Tao Hong, Joseph Roberts, and Pu Wang, "Weather data for energy analytics: from modeling outages and reliability indices to simulating distributed photovoltaic fleets," IEEE Power and Energy Magazine, vol.16, no.3, pp 43-53, May-June 2018. (Open AccessIEEE Xplore)


Weather Data for Energy Analytics

From Modeling Outages and Reliability Indices to Simulating Distributed Photovoltaic Fleets

Jonathan Black, Alex Hofmann, Tao Hong, Joseph Roberts, and Pu Wang

Abstract

Weather impacts virtually all facets of our daily life. As a result, many business sectors are affected by weather conditions, and the power industry is no exception. Weather is a major influencer on system reliability and a key driver of both power supply and demand. In this article, we will demonstrate novel uses of weather data for energy analytics via two utility applications. We first use easily accessible weather data together with regression analysis to model distribution outages and construct a probabilistic view of reliability indices that helps reveal a utility’s reliability trend. We then use high-resolution, commercial-grade weather data to develop realistic simulations of anticipated behind-the-meter photovoltaic (PV) fleets

Friday, April 20, 2018

Training Energy Data Scientists

Traditional power engineering curriculum has been heavily focusing on the engineering aspects of power systems, such as power flow, state estimation, stability and control. Data science has never been a focus in the past. I saw that gap 5 years ago, predicted the shortage of data scientists in the power industry, and left a great place to work to come back to academia. Nowadays, when other business sectors are offering 6-figure salaries to fresh graduates, utilities are having a hard time to compete on the analytics talents. Recently I had the opportunity to collaborate with Prof. David Wenzhong Gao from University of Denver and three other utility executives to put our thoughts in a paper.

Citation

Tao Hong, David Wernzhong Gao, Tom Laing, Dale Kruchten, and Jorge Calzada, "Training energy data scientists: universities and industry need to work together to bridge the talent gap," IEEE Power and Energy Magazine, vol.16, no.3, pp 66-73, May-June 2018. (IEEE Xplore)

Training Energy Data Scientists 

Universities and Industry Need to Work Together to Bridge the Talent Gap

Tao Hong, David Gao, Tom Laing, Dale Kruchten, and Jorge Calzada

Abstract

The workforce crisis is nothing new to the U.S. power industry. It has been a growing concern of both governments and industry organizations since the early 2000s. Meanwhile, the growth of data during the past decade has led to a demand surge for data analytics across all business sectors. The shortage of an electricity workforce and the increasing demand for data analytics present an emerging challenge as well as opportunity for university power engineering programs to bridge the data analytics talent gap. After gathering various perspectives from members of academia, industry, and government, we propose an interdisciplinary and entrepreneurial approach to revising the traditional power engineering curriculum for training the next generation of energy data scientists.

Saturday, April 14, 2018

Call For Papers: Forecasting for Social Good | International Journal of Forecasting

International Journal of Forecasting

Special Issue on Forecasting for Social Good

The area of forecasting and its link to decision making has been under research for decades. Whilst there have been many influential contributions seeking to examine the effects of forecasting under financial and economic motives, very little has been contributed (both in regular conferences and journal publications) on forecasting with social impact – that is forecasting for the social good, regardless of the financial implications, or optimizations attempted based on economic terms.

The International Journal of Forecasting (IJF) is excited to announce this Call For Papers for the special issue on “Forecasting for Social Good”. The purpose of the special issue is to attract high quality papers that are concerned with the social impact of forecasting.

Areas of interest include, but are not limited to:
  • Health and healthcare
  • Humanitarian operations
  • Disaster relief
  • Education
  • Social services
  • Environment
  • Sustainability
  • Sharing economy
  • Transportation
  • Urban planning
  • Fraud, collusion, and corruption
  • Government policy
  • Poverty
  • Privacy
  • Cyber security
  • Crime and terrorism

Submission Deadline: 31 October 2018.

Submission Guidelines:

To submit a paper for consideration for the Special Issue, please upload your paper online and include a cover letter clearly indicating that the paper is for the special issue “Forecasting for Social Good”. The webpage for online submission is mc.manuscriptcentral.com/ijf. Instructions for authors are provided at www.forecasters.org/ijf/authors . All papers will follow IJF’s double-blind refereeing process. For further information about the Special Issue, please contact the guest editors.

Guest Editors

Bahman Rostami-Tabar, Cardiff University, UK
Email: rostami-tabarb@cardiff.ac.uk

Michael Porter, University of Alabama, USA
Email: mporter@culverhouse.ua.edu

Tao Hong, University of North Carolina at Charlotte, USA
Email: hong@uncc.edu

Friday, April 13, 2018

Which Model is the Best?

Recently Rob Hyndman blogged about the history of forecasting competitions. I have read the post three times already. I learned something new each time. I wish every reader of this blog can also read that post and learn something from the history. Nevertheless, I would like to highlight a paragraph here:
[...] This reveals a view commonly held (even today) that there is some single model that describes the data generating process, and that the job of a forecaster is to find it. This seems patently absurd to me — real data comes from much more complicated, non-linear, non-stationary processes than any model we might dream up — and George Box famously dismissed it saying “All models are wrong but some are useful”.
I have been in the field load forecasting for a little more than 10 years. During the past decade, I received many "the best" questions: Which model is the best? Which technique is the best? Which software is the best? Which variable is the best? ...

There are some variants to these questions: Do you use neural networks? Do you use population in long term load forecasting? Do you use normal weather? Do you think demand response can reduce peak demand? Do you think a MAPE value of 20% is too high? ...

While the folks who asked these questions may expect a crisp answer of the best model, technique, software, variable, or a YES/NO answer, I had to disappoint them with "depends", sometimes followed by a lengthy elaboration.
It depends on the data, the business needs, the production environment, and many other factors... 
I don't have a single model to sell to my clients as "the best model". I recommend proper methodologies to the clients after making a comprehensive evaluation of their situations.

I think the root of these "the best" questions is that commonly held view:
There is some single model that describes the data generating process. 
I disagree with this view, and don't know where the view is originally coming from. I wish someone can write another history article to explain the source.

In load forecasting, there is no universally best model. We need empirical studies, many empirical studies, to show some evidence that one method is superior in some aspect. That's why I have been promoting reproducible research and benchmarking data pool and models.

In computational complexity and optimization, there is actually a theorem, no free lunch theorem

Sunday, March 25, 2018

Call For Papers: Data Analytics for Energy, Water, and Environment | IEEE Transactions on Engineering Management

IEEE Transactions on Engineering Management

Special Issue on Data Analytics for Energy, Water, and Environment

In 2008, the U.S. National Academy of Engineering proposed 14 grand challenges for engineering, of which five were related to energy, water, and environment. The world population has exceeded 7.6 billion in 2018, up from 6.7 billion in 2008. A common goal of the human beings is to meet the global need of energy and water without sacrificing the environment.

A growing interest in the recent decade is data analytics. Many industries have embarked analytics to extract actionable insights from the data and to make informed business decisions. This special issue of the IEEE Transactions on Engineering Management is aimed at highlighting the state of the art in data analytics applications in energy, water and environment.

The topics of interest include but are not limited to:
  • Quantitative models for water-energy nexus
  • Evaluating energy technology portfolios
  • Data integration and infusion for energy, water and environment applications
  • Cyber-physical systems for sustainable development
  • Power supply and air pollution
  • Building energy efficiency and indoor air quality
  • Energy efficiency analytics enabled by smart meter data
  • Clean energy/water access 
  • Energy systems planning under climate and water challenges
  • Risks and uncertainties in energy engineering projects
  • Energy management considering consumer behaviors
  • Sustainable supply chain design and inventory control
  • Water and energy management in sustainable agriculture
  • Environment, energy, and alternative transportation technologies

Submission Process

Please prepare the manuscript according to IEEE-TEM’s guidelines and submit it the journal’s Manuscript Central site. Please clearly state in the cover letter that the submission is for this special issue. 

Schedule

Interested authors send 2-page extended abstracts to the Corresponding Guest Editor by August 31st 2018
Decisions on acceptance of abstracts by November 30th 2018
Full papers submitted by May 31st 2019

Corresponding Guest Editor

Dr. Tao Hong (hong@uncc.edu)
Department of Systems Engineering and Engineering Management 
University of North Carolina at Charlotte

Guest Editors

Dr. Hua Cai, Purdue University
Dr. Gang He, Stony Brook University
Dr. Guiping Hu, Iowa State University
Dr. Yueming Qiu, University of Maryland
Dr. Ekundayo Shittu, George Washington University
Dr. Hongliang Zhang, Louisiana State University
Dr. Ning Zhang, Tsinghua University

Editor-in-Chief

Dr. Tugrul Daim, Portland State University