This foresight provides firm’s sufficient time to manage any asset maintenance required and reduce potential downtime or sudden malfunction. The solutions work by gathering data from existing sensors and feeding this data into a real-time predictive model. This model will calculates the likelihood of a machines failure and/or trigger an alarm if a certain threshold is surpassed.
Furthermore, energy production is often an extensive and complex process, comprised of many elements. Consequently, output quantities and quality may vary greatly and attempting to identify the sources of variation can be challenging.
Periods of downtime, due to unforeseen maintenance repairs or malfunctions, can have significant cost implications for companies and clients alike. In order to solve costly outages a predictive maintenance solution is able to detect seemingly minor anomalies and patterns of failure. This can help companies identify assets most likely to fail or have future problems.
The data mining experience and skill of the OLSPS Analytics’ team is in a prime position to aid firms with their energy production process by conducting an in-depth analysis on the comprehensive data produced by the various stages of production. We identify key correlations between production components and outputs, which allows optimisation resources to be allocated at the highest efficiency.
OLSPS Analytics can also provide energy companies with superior supply and demand forecasts. Through our unique micro-weather forecasting tool and analysing the numerous factors that influence energy consumption levels, we are able to establish the most significant variables and quantify their predictive power. Companies are thus able to avoid unnecessary expenses and maximize income potential through closer alignment of supply orders and demand levels.