Data Mining Practical Machine Learning Tools and Techniques

This chapter discusses leading problems linked to energy that the world is now confronting and to propose some ideas concerning possible solutions. Oil deserves special attention among all energy sources. Since the beginning of 1981, it has merely been continuing and enhancing the downward movement in consumption and prices caused by excessive rises, especially for light crudes such as those from Africa, and the slowing down of worldwide economic growth. Densely-populated oil-producing countries need to produce to live, to pay for their food and their equipment. If the economic growth of the industrialized countries were to be 4%, even if investment in the rational use of energy were pushed to the limit and the development of nonpetroleum energy sources were also pursued actively, it would be extremely difficult to prevent a sharp rise in prices. It is evident that it is absolutely necessary to pursue actively the development of coal, natural gas, and nuclear power if a physical shortage of energy is not to block economic growth.

World energy outlook

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

A review of deep learning for renewable energy forecasting

The Big Data revolution promises to transform how we live, work, and think by enabling process optimization, empowering insight discovery and improving decision making. The realization of this grand potential relies on the ability to extract value from such massive data through data analytics; machine learning is at its core because of its ability to learn from data and provide data driven insights, decisions, and predictions. However, traditional machine learning approaches were developed in a different era, and thus are based upon multiple assumptions, such as the data set fitting entirely into memory, what unfortunately no longer holds true in this new context. These broken assumptions, together with the Big Data characteristics, are creating obstacles for the traditional techniques. Consequently, this paper compiles, summarizes, and organizes machine learning challenges with Big Data. In contrast to other research that discusses challenges, this work highlights the cause–effect relationship by organizing challenges according to Big Data Vs or dimensions that instigated the issue: volume, velocity, variety, or veracity. Moreover, emerging machine learning approaches and techniques are discussed in terms of how they are capable of handling the various challenges with the ultimate objective of helping practitioners select appropriate solutions for their use cases. Finally, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement for further research in the field of machine learning with Big Data.

Machine Learning With Big Data: Challenges and Approaches

https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1153&context=electricalpub

An ensemble learning framework for anomaly detection in building energy consumption

Transfer learning with seasonal and trend adjustment for cross-building energy forecasting

Buildings are responsible for a significant amount of total global energy consumption and as a result account for a substantial portion of overall carbon emissions. Moreover, buildings have a great potential for helping to meet energy efficiency targets. Hence, energy saving goals that target buildings can have a significant contribution in reducing environmental impact. Today's smart buildings achieve energy efficiency by monitoring energy usage with the aim of detecting and diagnosing abnormal energy consumption behaviour. This research proposes a generic collective contextual anomaly detection (CCAD) framework that uses sliding window approach and integrates historic sensor data along with generated and contextual features to train an autoencoder to recognize normal consumption patterns. Subsequently, by determining a threshold that optimizes sensitivity and specificity, the framework identifies abnormal consumption behaviour. The research compares two models trained with different features using real-world data provided by Powersmiths, located in Brampton, Ontario, Canada.

Collective contextual anomaly detection framework for smart buildings

The advent of a new era of smart objects and networks of things inspired us to present a unified proposal for a model of a smart academic forum. It's an IoT architectural model that spans almost all vital divisions of the academic forum. We present IoT-architecture showing how academic services as well as data records could be delivered in an automated way to make academic life easier. This work elucidates the process of deriving the system concrete architectural model from IoT-A an outcome of European IoT-A project. A context view along with its IoT model is also presented. Finally, a connectivity model is described to show the interactivity among the main suggested architecture components.

Hany F. ElYamany

Papers

Machine Learning With Big Data: Challenges and Approaches

An ensemble learning framework for anomaly detection in building energy consumption

Transfer learning with seasonal and trend adjustment for cross-building energy forecasting

Collective contextual anomaly detection framework for smart buildings

IoT-academia architecture: A profound approach