A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources

Abstract

The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

Using Different ML Algorithms and Hyperparameter Optimization to Predict Heat Meters’ Failures

“…Therefore, we need to characterize the existing devices much better to be able to implement the idea of smart buildings. The knowledge needed for data analysis and model building is available to researchers and fairly well established [43]. Access to large datasets containing information about the operation of already installed devices in residential buildings is currently relatively easy.…”

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

“…As already mentioned, ANNs represent a powerful methodology for prediction and classification issues. Specifically, they enable condition analysis of the asset and of the operating variables, with the aim of anticipating potential problems [36].…”

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

“…All applications of ANNs can be classified into six typical groups of techniques: association (for reducing data dimensionality), classification (for grouping data into classes), conceptualization (for conceptualizing ideas based on concrete data), prediction (for predicting values), optimization (for seeking convergence to a minimum or maximum), and filtering (for sifting data according to restrictions) [48].…”

“…Many ANN applications are related to renewable energy sources (different uses of ANN models for better energy production predictions). Research addresses, for example, the use of ANNs to forecast solar radiation (the main problem for the best use of photovoltaic systems) and wind power forecasting [37,38,[48][49][50]. ANNs are applied for forecasting building energy usage and demand [34].…”