Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks

Abstract

The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neural network or long short-term memory network. Historical data related to energy consumption are used to construct a nested fuzzy cognitive map in order to better understand energy use behavior. Through the experiments, the usefulness of the nested structure in energy demand prediction is demonstrated, by calculating three popular metrics: Mean Square Error, Mean Absolute Error and the correlation coefficient. A comparative analysis is performed, applying classic multilayer perceptron artificial neural networks, long short-term memory networks and fuzzy cognitive maps. The results confirmed that the proposed approach outperforms the classic methods in terms of prediction accuracy. Moreover, the advantage of the proposed approach is the ability to present complex time series in the form of a clear nested structure presenting the main concepts influencing energy consumption on the first level. The second level allows for more detailed problem analysis and lower forecast errors.

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

“…However, the work of the application in a assisting PNS learning and development is still under covered, Through in-depth sentimental analysis, these inquiries promote a larger knowledge. Sentimental analysis is a technique that can provide a thorough understanding of people' sentiments and thoughts about a product or service [6,7]. Sentiment analysis will be utilized in this context to assess the quality of public service offered by the YuhSinau application.…”

A Review on Digital Twins and Its Application in the Modeling of Photovoltaic Installations

“…From the literature review in [40], regarding power forecasting models in photovoltaic systems [64,65], it is concluded that the best results were obtained with the deep learning approach. Furthermore, it is concluded that most of the current deep learning algorithms are used to predict power loads of non-renewable energy sources [66][67][68][69], and that studies related to the prediction of renewable energy based on meteorological data are scarce; therefore, in this study, the WPNet model based on deep learning was developed. This model first has the data processing layer, another GRU layer, and finally the Dense layer.…”

“…Therefore, many other studies investigate the detection and diagnosis of failures of different components in photovoltaic systems [85][86][87][88][89], such as solar panel breakage (micro-cracks), broken solar cells or bypass diodes, wiring failures, potential induced degradation (PID) and short-circuit problems in power converters [88,89]. Work on the detection and classification of faults in PV installations using artificial intelligence techniques has been increasing [90][91][92], and not all studies include a system to alert the user when a failure occurs [69][70][71][72]. In [22], it was reported that DT technology was used for the operation and maintenance of solar energy systems, according to several studies, or the life cycle management of the solar plant [93], or for the monitoring of decentralised renewable energy sources [94].…”

The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins

“…They used the average temperature as an input variable to predict electrical loads for residential communities through the GRU model. Poczeta et al [22] combined fuzzy cognitive maps and artificial neural networks to construct an energy usage power load forecasting model. Pombo et al [23] integrated a PV performance model into a machine learning algorithm model to predict PV power using physical information features related to PV power generation.…”

“…Most of the existing deep learning algorithms are used for predicting power loads from non-renewable energy sources [17,18,20,22], while there is not enough research related to predicting renewable energy power based on weather data. Refs.…”