Predictive maintenance of electrical grid assets: internship at EDP Distribuição - Energia S.A

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis report will describe the activities developed during an internship at EDP Distribuição, focusing on a Predictive Maintenance analytics project directed at high voltage electrical grid assets including Overhead Lines, Power Transformers and Circuit Breakers. The project’s main goal is to support EDP’s asset management processes by improving maintenance and investing planning. The project’s main deliverables are the Probability of Failure metric that forecast asset failures 15 days ahead of time, estimated through supervised machine learning models; the Health Index metric that indicates asset’s current state and condition, implemented though the Ofgem methodology; and two asset management dashboards. The project was implemented by an external service provider, a consultant company, and during the internship it was possible to integrate the team, and participate in the development activities

Predictive analytics applied to firefighter response, a practical approach

Time is a crucial factor for the outcome of emergencies, especially those that involve human lives. This paper looks at Lisbon’s firefighter’s occurrences and presents a model,based on city characteristics and climacteric data, to predict whether there will be an occurrence at a certain location, according to the weather forecasts. In this study three algorithms were considered, Logistic Regression, Decision Tree and Random Forest.Measured by the AUC, the best performant modelwasa random forestwith random under-sampling at 0.68. This model was well adjusted across the city and showed that precipitation and size of the subsection are themost relevant featuresin predicting firefighter’s occurrences.The work presented here has clear implications on the firefighter’s decision-makingregarding vehicle allocation, as now they can make an informed decision considering the predicted occurrences

Predicting residential building age from map data

The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points