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How Hadoop-Oriented Smart Buildings Drive Energy Efficiency

The advent of Hadoop-oriented smart buildings has transformed the way we work and live. It has also influenced the design and networking of utilities infrastructures.

Specifically, internet-connected devices that collect and share data are now widely used in commercial and residential structures, creating communities of smart buildings.

Benefits of Hadoop-Oriented Smart Buildings

Smart technology allows building managers to automate energy use, security & energy system integration, utilities use, and even the management of elevators and escalators. Over the next five years, this technology is expected to grow by 34 percent annually, with a predicted total market value of nearly $25 billion by 2021.

There are several advantages to employing smart building technology.

  • Facilitates Data Collection and Real-Time Monitoring
    Sensors collect data on many functions, from water usage to electricity consumption, which helps building managers calibrate systems to achieve operational efficiency.
  • Increases the Building’s Asset Value
    Buildings with IoT-enabled technology increase in value since they are well-maintained, resulting in less depreciation over time.
  • Helpful for the Environment
    Smart buildings follow green standards, are energy efficient, and are good for the environment.
  • Enables Healthier Lifestyles and Working Spaces
    Smart buildings provide a more comfortable, soothing, and healthful environment using advanced sensor-controlled lighting and climate controls. Studies show that companies with smart buildings enjoy improved productivity rates and greater employee satisfaction.
  • Provides Operational Cost Savings
    Smart systems can monitor and adjust lighting, air conditioning, and heating use as needed. This reduces overall operational costs. These actions also save on wear-and-tear equipment expenses over time.

The Role of OLAP In Hadoop-Oriented Smart Buildings

Online Analytical Processing (OLAP) plays a substantial role in multi-dimensional analysis and business data interaction. However, with the explosion of Big Data, OLAP could not effectively handle massive amounts of incoming data in a standalone environment.

In order to address this issue, developers introduced the concept of using OLAP on Hadoop.

Hadoop can efficiently store and process enormous amounts of unstructured data at higher speeds. It can do so over a distributed system without expensive proprietary hardware. Combining Hadoop technology with OLAP provides a more efficient approach to managing Big Data from the building’s network of sensors.

Now, facility managers can harness essential data, once considered unusable, to monitor and control their smart building systems.

Big Data Analytics In Building Construction And Engineering

With the construction sector suffering from a sluggish annual productivity improvement rate of only 1%, engineering and construction (E&C) firms are looking for data-driven solutions.

Many project teams in the building construction and engineering sector are turning to Big Data analytics to make better management decisions. McKinsey documents some significant ways Big Data analytics is driving critical decisions.

Calculating Accurate Bid Estimates

A significant challenge in the selection of project subcontractors is incomplete information. The average 5-10 year project timeline contributes to the difficulty of defining project scope.

Overestimating a project can result in the loss of a contract while underestimating can result in financial losses. In response to this problem, data modeling is used to analyze historical information, such as labor and contract arrangements, spending trends, and project size.

This analysis helps the team to focus on a more accurate spending target for bid calculations.

Evaluating Bids From Subcontractors

E&C firms receiving bids from subcontractors rely on procurement specialists and project managers to assess their feasibility. Unfortunately, this method often results in inaccurate decisions due to a lack of empirical data.

Using analytics such as previous project final costs, E&C companies can more accurately determine project costs when considering subcontractor bids. Companies can build historical databases of previous work costs and use this data when assessing bids.

Assess Progress and Track Potential Problems

Analytical tools can track data and assess infrastructure efficiency, so building managers can react quickly to red flags before a problem occurs.

Design, Build, and Operate Systems

Big data analytics are used to design structures and determine building location. For example, Rhode Island’s Brown University used data analysis to decide where to build a new engineering facility to maximize student benefit.

Weather, traffic, and community data can help determine when and what construction equipment should be leased or purchased. The data can also determine how to use fuel more efficiently. Data harvested from sensors track performance levels and determine whether energy conservation measures are conforming with building goals.

Work With Entrance to Optimize Your Facility

IoT continues to significantly contribute to the development and evolution of smart building technology. Big Data analytics is not only crucial in maintaining operational efficiency but also in engineering these structures.

Need to optimize your facility? Entrance is the #1 most trusted software consulting and development company in North America. Our software tools enable projects to run smoothly and efficiently, keeping your company a step ahead of the competition.  Contact us for more information on how our data management and analytics solutions can increase your construction or engineering firm’s productivity levels.

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