The evolution over time of energy consumption data and the production processes of a factory can provide valuable information on energy usage, and its monitoring makes it possible to identify and predict energy behaviour patterns to optimise industry resource planning. Predictive management is one of the best options for optimising operations and adjusting energy contracts (smart contracts).

BigDa Solutions is working to harness the power of industry data and revolutionise energy management with BEMP: an innovative platform based on big data and artificial intelligence to monitor, control and predict the consumption of electricity, natural gas, water and compressed air.

The digitalisation of consumption as a starting point

Digitisation allows us to obtain real-time energy data and know what is happening at any moment.

Due to the difficulty of collecting data continuously and automatically, industrial companies have been reading consumption on a one-off basis (generally manually), either on a daily, weekly or even monthly frequency. The lack of continuous information hinders knowing the energy cost per unit produced, as it does not differentiate the variations in consumption associated with sub-processes and the different phases of production, so it cannot determine the energy cost of production per unit.

Real-time data digitisation and processing allow industries to track what is happening in consumption terms by providing insights into the energy cost evolution for each product and associating it with each stage of the manufacturing process. However, digitisation is only the first step in analysing the progress of energy consumption over a day in the industry.

Energy usage digitisation allows moving from a non-continuous data record to real-time automatic metering. The most common scenario is from capturing one daily, weekly or monthly record to a default 96 daily records (if acquired in 15-minute sequences), or more depending on the defined frequency. This information makes it possible to know precisely how energy consumption has fluctuated and how factories' installations consume energy.

How to improve energy management through forecasting?

Acquisition and digitisation are only one phase in the data lifecycle, but data must go through different stages to gain energy insights that improve a factory's energy efficiency:

Before digitising energy meters, the first step is to define what data should be acquired and monitored, and to do so; it is important to bear in mind that not all data adds value to a company’s energy efficiency strategy. Business and measurement targets need to be set to obtain the relevant information for energy management.

The second step is to filter the data to achieve optimal quality, a key factor for developing highly accurate predictive models. Thus, the objective is to obtain as much data of the best possible quality to have a historical record representing the different situations that can occur in a factory, area, or production line. The raw data acquired must be filtered, processed and transformed into information since, in most cases, the raw figures (such as the incremental values of the meters) do not indicate what is happening at any given moment unless they are preprocessed, for example, using filters to de-accumulate consumption by time step.

BigDa Solutions focuses on making the data lifecycle as automatic, stable and secure as possible to provide a high-quality and reliable real-time service to make the best use of energy resources.

After data acquisition and filtering, the third step is to create predictive models using artificial intelligence to forecast energy consumption (forecasting). These BEMP-generated forecasts are highly customisable non-code solutions. With this tool, companies can improve their energy efficiency through comparisons and simulations, both in the past and in the future without coding.

Predictive models have been used in the past to estimate how much energy should have been consumed in a given installation based on specific conditions during a particular period. By comparing these estimates with actual values, energy inefficiencies such as over- or under-consumption can be identified, leading to improvements in operational processes and the development of management methods to optimize energy efficiency. In contrast, future forecasts focus on predicting the energy consumption of factories or specific areas or production lines. This helps to improve energy management and planning by anticipating how much energy will be required to meet production goals and capacity requirements.

The fourth step is visualising the information and results from the predictive models in different formats to understand their evolution, accuracy and behaviour better. This step focuses on achieving actionable energy insights that inform the energy performance of industrial companies. In this phase, business intelligence methodologies are applied for defining KPIs and generating reports with the primary objective of knowing what is happening in detail in each process and comparing actual consumption with the predictions of artificial intelligence models. In addition, alarms and notifications can be configured to be always informed about what is happening in the company.

Big data and artificial intelligence for a predictive energy future

As we move towards Industry 4.0, energy efficiency is becoming increasingly important. A predictive management model powered by digitalisation, advanced analytics, and AI forecasting, is making this possible. With the help of big data, industries are now able to better control their energy consumption by leveraging their own data.

BigDa Solutions' mission is to analyse the correlation between energy consumption and the productive process.

Data draws a consumption map and through AI processing, the predictive models can establish historical comparisons and future simulations to analyse energy efficiency improvement opportunities, or in other words, to help industrial companies produce more while consuming less.

Find out now how to save energy and predict your energy consumption of tomorrow.

Transform your energy consumption today with big data