Demand Forecasting Methods, Tools with Machine Learning

Introduction to Energy Supply and Demand Forecasting

Forecasting of Energy Supply performs a vital role in the electric industry, as it gives the basis for giving decisions in power system planning and operation. A numerous variety of techniques for predicting power demand are being used by electrical firms, which are appropriate to short-term, medium-term or long-term forecasting. In such a changing environment common forecasting techniques are not sufficient, and more advanced methods are required.

The aim is to completely untangle all the circumstances that lead to demand change and to discover the underlying problems. But analyzing many personal and social factors is difficult. We need both intense data, along with multiple predictive models to analyze it.

Maintenance of energy consumption is an important task to obtain a sustainable environment and address

Increasing population
Hiked incomes
Raised revenues
Industrialization
The scarcity of natural resources

Prediction of energy consumption makes the decisions for energy purchase and generation using various algorithms. According to the need, predictions made on an hourly, monthly or daily basis. Gaussian process regression and Linear regression models used.

Process of Energy Supply and Demand Forecasting

Energy consumption measured in Watt per Hour whereas demand weighed regarding work done in 15–30 minutes.

PUE is the proportion of the amount of power required to operate and cool the data station versus the volume of power extracted by the IT equipment in the data hub. The equation is -

PUE = (Total Facility Energy) / (IT Equipment Energy)

Major Challenges in Energy Supply and Demand Forecasting

Real Data Collection for machines and pipelines
Power Consumption collection
The data for the machine and features to predict the power consumed

Solution Offerings for Energy Demand Forecasting

Multiple IoT devices installed on different machines will emit the data to the Apache Nifi, routing the data to various sources.
Data Pipeline built using Apache Kafka, processed using Apache Spark, and stored in HBase, or Cassandra.
Creation of historical data using Apache Nifi to route data to S3
Spark SQL and Graph Database for Pattern and Link Analysis