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Better Smart Green Technologies with Big Data

Big Data Analytics 7

Each country’s effort to respond to climate change became an important global issue at the United Nations Climate Change Conference 2015 recently held in Paris. As major corporations as well as countries that signed the agreement are now under evaluation for their adaptations to climate change, their investments in the energy field, which take a large proportion in the total greenhouse gas emission, are also increasing. The investments are especially concentrating on eco-friendly fuel conversion, distributed generation, and higher energy efficiency.

Such efforts also include utilizing big data in smart green technologies. Today, let’s have a look at how big data is used in smart green technologies, focusing on environmental preservation, responses to climate change, smart cities, and smart grids.

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Smarter Environmental Preservation and Climate Change Adaptation with Big Data

We are witnessing easy and convenient big data solutions created for average users these days. Through these solutions, environment related data regarding issues like marine ecosystems, climate change, disasters, and diseases are being combined with data from geography, social science, natural science, and policy for comprehensive analysis and visualization. Since big data analysis reveals the links between issues we could not connect before, it is finally becoming possible to detect the signs of serious environmental issues beforehand and respond to them systematically.

One example is Conservation International (henceforth CI), an American environmental organization which is cooperating with HP in order to monitor and research biodiversity in tropical rain forests using big data.

Another international organization called the World Resources Institute (hereafter WRI) developed the Global Forest Watch system and opened it to the public through their website and smartphone app. This system applied Google’s cloud-based big data infrastructure and analysis algorithm to reduce operational costs and enhance performance in forest destruction monitoring and analysis services.

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Global Forest Watch website and smartphone app.

Solving the Smart City’s Eco-friendly Issue with Big Data

Smart Water Management

Many cities around the globe are beginning to establish smart water management systems, which utilize IT throughout the entire water resource management procedure, as a part of larger smart city projects. The social atmosphere in which health related topics become serious issues, as well as water related problems caused by climate change, decrepit facilities, and pollution brought these green projects forward.

These projects are now introducing various technologies such as real-time water management and leakage detection which combines real-time data from smart meters and sensors with climate data for analysis, pollution forecasts based on climate change, and customized water resource management established according to the supply and demand forecast. Many cities are combining big data to their smart water systems for safe and effective water supplies.

For example, the Netherlands adopted a big data solution which integrates information separately created for floods, droughts, and water quality control for better management and analysis. Spain also uses real-time big data analysis for water quality monitoring, chlorine ratio optimization, and leakage detection. Koryung-gun and Paju City in Korea are operating a trial project called Smart Water City as well, which controls the quality and quantity of water with IT throughout all stages and opens the information about tap water to the public.

Air and Garbage Control

There are other various environmental problems that threaten our comfortable and safe urban lives. Various city infrastructures such as garbage cans, street lights, traffic lights, and water and sewage systems are being combined with two way communication infrastructures to take care of these problems using sensors and smart devices. Big data utilization from these sensors and devices are dramatically improving intelligent urban management services and energy efficiency.

Beijing, where one of the biggest concerns is air pollution, applied big data technology for a project called Green Horizon which is being operated in accordance with IBM in order to reduce air pollution. Virtual satellite data as well as other related forms of big data on various factors that affect the air quality are collected and analyzed through their intelligent solution. By figuring out what is causing the spread of air pollutants and whether there are any diffusion patterns, the system can forecast the air quality in Beijing for up to 72 hours.

Chicago analyzes the data collected from sensors installed on public trash cans in order to optimize garbage collection cycles. They install diverse sensors which measure temperature, humidity, level of illumination, noise, and air pollution throughout the city, and collect, integrate, and analyze the big data to utilize it for their urban management and policy development. Their big data is also open for public use.

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New regeneration energy forecast system created for the Green Horizon project in Beijing

Innovating Smart Grids with Big Data

The smart grid industry, which adopts top notch IT for power generation and supply chains to expand the use of eco-friendly energy sources and aims to improve the efficiency in the generation, supply, and operation systems, is growing fast these days. A smart grid is an intelligent power network that uses two way communication, smart meters, and facility sensors. The power supplier can monitor current generation and demand status as well as facility status on the network in real time while checking power consumption easily through the remote meter reading function.

As smart grids are being distributed rapidly, it has become possible to integrate, analyze, and utilize real-time power generation and consumption data, as well as other kinds of various environmental data. The improvement in energy efficiency and intelligent services is expected to result in higher investment efficiency of the existing smart grid infrastructure.

Big Data in Power Generation, Transmission and Distribution

Collecting and analyzing the long-term environmental data in a particular region can be extremely helpful when choosing the most effective and economic location for renewable energy generation such as solar and wind power. Analyzing big data from sensors attached to facilities will also enable facility interruption forecast and fast error detection, which can reduce maintenance expenses and minimize time spent for repair so that a facility can start its operation as soon as possible.

Vestas Wind Systems, a Danish wind power supplier, analyzes big data on weather, tides, satellite images, and geographical data, in order to select the best location for generators with the highest generation efficiency and lowest energy expenditure, and to optimize the maintenance cycle.

Big Data in Energy Consumption

The use of big data in the field of energy consumption is mostly concentrated on the reduction in energy expenditure and better management efficiency, especially through demand response, microgrids, and xEMS.

Demand Response

The American firm EnerNoc, which is the world’s biggest power management company, monitors and analyzes the clients’ energy consumption data from tens of thousands of demand management equipment installed in buildings through their big data technology. They also analyze large scale data streams such as their clients’ power consumption, rate, and weather to provide real-time energy intelligent services while operating optimal demand source portfolios which suit diverse demand response markets around the globe.

Microgrids

A microgrid is a small-scale electricity network which uses renewable energy and distributed power to self-generate, and/or utilizes Energy Storage System (ESS) to lower the electricity rate. Because economic and stable operation of self-running generation and consumption circles is the most important aspect of a microgrid, precise forecasting of generation and consumption as well as optimized operation are considered crucial in microgrid development.

Big data technology for optimal microgrid operation is applied to multiple projects in Korea, such as the self-energy sufficient island project on Ulleungdo Island run by LG and the campus microgrid technology development project created by the Ministry of Trade, Industry and Energy.

Energy Management

Big data based real-time electricity monitoring and analysis services are being introduced to the energy management sector. These include collecting power consumption data based on the cloud network as well as analyzing the pattern for each place or facility, forecasting the future consumption, checking the actual consumption to see the accuracy of the forecast, and providing information on ineffective power expenditure by comparing the building/facility’s pattern to other similar ones.

Another service is the facility maintenance service, which detects lowered efficiency of facilities consuming lots of energy as early as possible, so it can be fixed or replaced without wasting more energy. Global energy solution companies like GE, Siemens, and Schneider are actively developing these technologies.

LG’s Prospects in the Energy Market

LG is the only company in Korea which has an original value chain for eco-friendly energy from generation to storage to consumption, with the solar power module and ESS from LG Electronics, ESS battery from LG Chemical, and the smart grid from LG CNS.

Efforts to create inventive premium value in the field of energy are accelerating by combining their capacity in data analysis with LG CNS’ big data solution which proved its excellence through various projects in manufacturing, communication media, distribution, and banking.

Advancement of the big data based functions for LG CNS energy solutions which are being applied to the field of ESS, energy management, and microgrids is especially remarkable. I look forward to seeing these solutions being applied in more various fields with more accurate generation and consumption forecasting and improved error detection functions.

Written by Hye Jin Rhou, LG CNS Big Data Business Unit

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