Advanced Analytics
Advanced Analytics and Predicitve Modelling
Predictive analytics uses historical data to predict future events. Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next, or to suggest actions to take for optimal outcomes.
Predictive analytics has received a lot of attention in recent years due to advances in supporting technology, particularly in the areas of big data and machine learning.
Rise of Big Data
Predictive analytics is often discussed in the context of big data, Engineering data, for example, comes from sensors, instruments, and connected systems out in the world. Business system data at a company might include transaction data, sales results, customer complaints, and marketing information. Increasingly, businesses make data-driven decisions based on this valuable trove of information.
Increasing Competition
With increased competition, businesses seek an edge in bringing products and services to crowded markets. Data-driven predictive models can help companies solve long-standing problems in new ways
Companies also use predictive analytics to create more accurate forecasts. These forecasts enable resource planning to be done more effectively.
Cutting-Edge Technologies for Big Data and Machine Learning
To extract value from big data, businesses apply algorithms to large data sets using tools such as Hadoop and Spark. The data sources might consist of transactional databases, equipment log files, images, video, audio, sensor, or other types of data. Innovation often comes from combining data from several sources.
With all this data, tools are necessary to extract insights and trends. Machine learning techniques are used to find patterns in data and to build models that predict future outcomes. A variety of machine learning algorithms are available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.
Step-by-Step Workflow for Predicting Energy Loads
Typically, the workflow for a predictive analytics application follows these basic steps:
- Import data from varied sources, such as web archives, databases, and spreadsheets.
Data sources include energy load data in a CSV file and national weather data showing temperature and dew point. - Clean the data by removing outliers and combining data sources.
Identify data spikes, missing data, or anomalous points to remove from the data. Then aggregate different data sources together – in this case, creating a single table including energy load, temperature, and dew point. - Develop an accurate predictive model based on the aggregated data using statistics, curve fitting tools, or machine learning.
Energy forecasting is a complex process with many variables, so you might choose to use neural networks to build and train a predictive model. Iterate through your training data set to try different approaches. When the training is complete, you can try the model against new data to see how well it performs. - Integrate the model into a load forecasting system in a production environment.
Once you find a model that accurately forecasts the load, you can move it into your production system, making the analytics available to software programs or devices, including web apps, servers, or mobile devices.
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