Book Image

Hands-On Artificial Intelligence for IoT - Second Edition

By : Amita Kapoor
Book Image

Hands-On Artificial Intelligence for IoT - Second Edition

By: Amita Kapoor

Overview of this book

There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence.
Table of Contents (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Electrical load forecasting in industry


Electricity is presently the most important energy vector in both the domestic and industrial sectors. Since, unlike fuels, it is hard and expensive to store electricity, there is a need for a precise coupling between its generation and demand. Electrical energy load forecasting, hence, is very vital. Depending upon the time range (forecasting horizon) electrical load forecasting is classified into the following three categories:

  • Short-term load forecasting: The forecast is made for one hour to a few weeks
  • Medium-term load forecasting: The forecast duration spreads from a few weeks to a few months
  • Long-term load forecasting: Here, the forecasting is done from a few months to years

Depending upon the need and application one may have to plan either one or all of the previous load forecasting categories. In recent years, a lot of research work has been done in the area of short-term load forecasting (STLF). STLF can assist industries by providing an accurate...