世界各国のリアルタイムなデータ・インテリジェンスで皆様をお手伝い



デジタル公益事業者のマシンラーニング(機械学習)

Machine Learning for the Digital Utility

 

出版社 出版日電子媒体価格ページ数図表数
Guidehouse (旧Navigant Research)
ガイドハウス
2017年12月 US$ 1,800
1-5ユーザライセンス(PDF)
15p2点

サマリー

米国調査会社ナビガントリサーチ(Navigant Research)の調査レポート「デジタル公益事業者のマシンラーニング(機械学習)」は、マシンラーニング(機械学習)の利用事例をあげ、顧客セグメンテーション、価格予測、異常検知、不正検出、予知保全などにおいて、既存の解析技術に比べて優れている点を査定している。特に分散型エネルギー供給(分散発電、DER)の管理やトランザクティブエネルギーなどにおける機械学習の今後の必要要件についても論じ、公益事業者が機械学習戦略を発展させるうえでの助言も行っている。

目次(抜粋)

  • 人工知能(AI)時代の機械学習(ML)
  • エネルギー市場における機械学習の発展
  • エネルギー市場における機械学習の多くの利用事例
  • 機械学習活用への助言

It is hard to escape the hype surrounding artificial intelligence (AI). Blockchain is possibly the only other technology to command as many headlines as AI. Yet, there is a tangible difference between what is written about the two technologies. On the one hand, despite plenty of issues with blockchain, many in the media still effusively claim it will revolutionize the world in the same way as the internet. AI receives similarly gushing praise from some quarters—typically in technology and trade press, which extol the virtue of an increasingly automated future—but can be heavily criticized in mainstream press as a threat to employment or civilization itself.

Machine learning is an AI technology that is rapidly moving into the mainstream and is high on the agenda of many utilities. This report discusses machine learning in the context of other AI and analytics technologies, provides a brief description of how it works, and examines some recent high profile achievements. While machine learning is not new in parts of the utility value chain, despite some inherent limitations to the technology, various drivers will bring it out of pockets of excellence and thrust it into many other areas of the business.

This Navigant Research report describes several use cases for machine learning and examines why machine learning has an advantage over existing analytics techniques, including customer segmentation, pricing forecasts, anomaly detection, fraud detection, and predictive maintenance. Future requirements for machine learning—specifically for distributed energy resources (DER) management and transactive energy—are also discussed, as are several recommendations for utilities developing their machine learning strategies.

Key Questions Addressed:
  • What is machine learning?
  • How do machine learning and artificial intelligence (AI) differ?
  • Why does machine learning get so much good and bad press?
  • Which use cases are most suited to machine learning?
  • Where could machine learning be applied in the future?
Who needs this report?
  • Hardware vendors
  • Software vendors
  • Analytics vendors
  • Utilities
  • Regulators
  • Trade unions
  • Investor community


目次

1. Executive Summary

2. Machine Learning in the Age of AI

2.1   Is Machine Learning AI? More Importantly, Does It Matter?

2.2   Historic Definitions of Machine Learning

2.3   Machine Learning Relies on High Quality Data

2.4   Training Sets Machine Learning Apart from Other Analytics

2.5   Machine Learning Will Rapidly Become Deep Learning

3. Machine Learning Is Coming of Age in the Energy Industry

3.1   Technology Advancements Make the Case for Machine Learning

3.2   The Market Becomes More Open to Machine Learning

3.3   Machine Learning Is Not, and Never Will Be, a Panacea

4. The Many Use Cases for Machine Learning in Energy

4.1   Clustering

4.2   Regression

4.3   Classification

4.4   Future Energy Markets Will Increasingly Rely on Machine Learning

5. Recommendations

5.1   Manage Employees’ Antipathy to AI

5.2   Remember that Machine Learning Has Limitations

5.3   Place Machine Learning in the Context of Other Analytics Tools

5.4   Procure Machine Learning as Part of a Wider Strategy

5.5   Bake Data Management and Security into Analytics Strategies from the Outset

List of Charts and Figures

  • Analytics Maturity
  • New Products and Services Have Increasing Demand for Analytics

 

COPYRIGHT(C) 2011-2020 DATA RESOURCES, Inc. ALL RIGHTS RESERVED.