Energy Informatics Using the Distributed Ledger Technology and Advanced Data Analytics

Energy Informatics Using the Distributed Ledger Technology and Advanced Data Analytics

Umit Cali (University of North Carolina at Charlotte, USA) and Claudio Lima (Blockchain Engineering Council, USA)
Copyright: © 2020 |Pages: 44
DOI: 10.4018/978-1-5225-8559-6.ch016


The main drivers of the third industrial revolution era were the internet technologies and rise of renewable and distributed energy technologies. Transition to green and decentralized energy resources and digital transformation of the existing industrial infrastructure had been the biggest achievements of the third industrial revolution. The main drivers of the fourth era will be artificial intelligence (AI), quantum computing, advanced biotechnology, internet of things, additive manufacturing, and most importantly, distributed ledger technology (DLT). Energy forecasting such as wind and solar power forecasting models are the most common energy AI-based informatics applications in the energy sector. In addition, use of DLT is expected to be an industrial standard in various industrial sectors including energy business in the coming decade. This chapter emphasizes description of energy forecasting using AI and energy DLT and future developments and solutions to overcome challenges that are associated with standardization of the energy DLT applications.
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Setting The Stage

Currently, a major part of the world’s energy need is satisfied by non-renewable fossil fuel resources such as coal, oil, and natural gas which are well known to release a large number of greenhouse gasses. However, use of clean and carbon-free energy resources such as wind and solar have become more dominant in the global energy mix over the last two decades (Cali, 2018). The forms of renewable energy, such as wind and solar power, are among the fastest growing forms in terms of use in countries like China, the United States, India, and Germany. Wind and solar power are different from non-renewable fossil fuel resources, since the “fuel” cannot be stored until such time that its power is needed in the power system. Besides, most of the decentralized energy resources are in the form of intermittent renewable energy resources (RES) which make it challenging to predict their power outputs (Cali, 2010; Eltawil et al., 2010). Concentrating solar power (CSP) and large-scale hydropower plants are exceptional in terms of intermittency and controllability, as the heat produced by the CSP plants and the water in the hydro-electric dams can be stored for several hours and be used for power generation when it is needed. Moreover, utilization of energy storage units in parallel to higher penetration of RES are becoming more popular and constitute an economically viable option to integrate the higher number of intermittent RES which can also be used to store electrical power and be operated to provide various services such as time-of-use bill management, demand charge reduction, energy arbitrage, frequency regulation, and peak shaving.

Key Terms in this Chapter

Artificial Intelligence: Artificial intelligence (AI) is man-made intelligence used and demonstrated by machines and devices in contrast to biological intelligence.

Blockchain: Blockchain is a decentralized, immutable, secure data repository or digital ledger where the data is chronologically recorded. The initial block named as Genesis. It is a chain of immutable data blocks what has anonymous individuals as nodes who can transact securely using cryptology. Blockchain technology is subset of distributed ledger technology.

Renewable Energy Forecasting: Renewable energy forecasting is sub-category of energy forecasting which focuses on the forecasting of renewable energy resources’ output in various forecasting horizons between a couple of seconds to multiple years.

Prosumers: Prosumers are the individuals or entities who produces and consumes a product or good at the same time. People have moved being only energy consumers to energy producers especially with the help of the roof top mounted behind the meter (BTM) PV panels. Prosumer terminology is not limited to PV generation or individual persons but also it may cover the any type of distributed energy generation, energy storage services and commercial companies as well.

Distributed Ledger Technology: Distributed ledger technology (DLT), in particular blockchain, is emerging technologies which is based on a consensus mechanism where the digital records or ledgers are stored in decentralized repository in immutable manner. DLT is supported by advanced encrypted algorithms to satisfy the cyber-security requirements and build the trust. DLT is designed to eliminate central authority to validate and store the ledgers. This system also eliminates the unnecessary third parties and reduce the business costs.

Numerical Weather Prediction: Numerical weather prediction (NWP) data consists of predictions of meteorological variables such as wind speed, wind direction, temperature. Pressure and solar radiation parameters for the next a couple of days. NWP data is the most essential dataset for energy forecasting systems.

Smart Contract: Smart contracts are the digital equivalent of a legal contract between two parties or nodes in digital world. Smart contracts are self-executing contracts with the term of agreement written in to a code within a distributed ledger technology network

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