• Energy Research

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

Abstract Energy consumption has increased in recent decades at a rate ranging from 1.5% to 10% per year in the developed world. As a consequence, several efforts have been made to model energy consumption in order to achieve a better use of energy and to minimize environmental impact. Open problems in this area range from energy consumption forecasting to user profile mining, energy source planning, to transportation, among others. To address these problems, it is important to have suitable tools to model energy consumption data series, so that the analysts and CEOs can have knowledge about the underlying properties of the power demand in order to make high-level decisions. In this paper, we focus on the problem of energy consumption modelling, and provide a solution from the perspective of symbolic regression. More specifically, we develop hybrid genetic programming algorithms to find the algebraic expression that best models daily energy consumption in public buildings at the University of Granada as a testbed, and compare the benefits of Straight Line Programs with the classic tree representation used in symbolic regression. Regarding algorithm design, the outcomes of our experimentation suggest that Straight Line Programs outperform other representation models in the symbolic regression problems studied, and also that the hybridation with local search methods can improve the quality of the resulting algebraic expression. On the other hand, with regards to energy consumption modelling, our approach empirically demonstrates that symbolic regression can be a powerful tool to find underlying relationships between multivariate energy consumption data series.

Abstract Electricity demand is shown to steadily increase in the last few years, and it is one of the key aspects of living standards and quantifying welfare effects. However, the irregularity of electricity demand is one of the main problems in this field. Therefore, it is important to accurately anticipate future expenditures in order to optimize energy generation and to avoid unexpected wastes. As a result, we developed Machine Learning models to predict electricity demand. In particular, our study has been performed using data of the Spanish Electricity Network from 2007 to 2019. To this end, we propose the implementation of a set of Machine Learning techniques using various frameworks. In particular, we implemented six different prediction models: Linear Regression, Regression Trees, Gradient Boosting Regression, Random Forests, Multi-layer Perceptron, and three types of recurrent neural networks. Our experimentation shows promising results in all cases, since our models provides better prediction than the one estimated by the Spanish Electricity Network with an improvement of 12% in the worst case and up to 37% for the best predictor, which turned out to be the Gated Recurrent Unit neural network.

Abstract In the Energy Efficiency field, the incorporation of intelligent systems in cities and buildings is motivated by the energy savings and pollution reduction that can be attained. To achieve this goal, energy modelling and a better understanding of how energy is consumed are fundamental factors. As a result, this study proposes a methodology for knowledge acquisition in energy-related data through Time-Series Clustering (TSC) techniques. In our experimentation, we utilize data from the buildings at the University of Granada (Spain) and compare several clustering methods to get the optimum model, in particular, we tested k-Means, k-Medoids, Hierarchical clustering and Gaussian Mixtures; as well as several algorithms to obtain the best grouping, such as PAM, CLARA, and two variants of Lloyd’s method, Small and Large. Thus, our methodology can provide non-trivial knowledge from raw energy data. In contrast to previous studies in this field, not only do we propose a clustering methodology to group time series straightforwardly, but we also present an automatic strategy to search and analyse energy periodicity in these series recursively so that we can deepen granularity and extract information at different levels of detail. The results show that k-Medoids with PAM is the best approach in virtually all cases, and the Squared Euclidean distance outperforms the rest of the metrics.

Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.

The energy supply sector faces significant challenges, such as the ongoing COVID-19 pandemic and the ongoing conflict in Ukraine, which affect the stability and efficiency of the energy system. In this study, we highlight the importance of electricity pricing and the need for accurate models to estimate electricity consumption and prices, with a focus on Spain. Using hourly data, we implemented various machine learning models, including linear regression, random forest, XGBoost, LSTM, and GRU, to forecast electricity consumption and prices. Our findings have important policy implications. Firstly, our study demonstrates the potential of using advanced analytics to enhance the accuracy of electricity price and consumption forecasts, helping policymakers anticipate changes in energy demand and supply and ensure grid stability. Secondly, we emphasize the importance of having access to high-quality data for electricity demand and price modeling. Finally, we provide insights into the strengths and weaknesses of different machine learning algorithms for electricity price and consumption modeling. Our results show that the LSTM and GRU artificial neural networks are the best models for price and consumption modeling with no significant difference.

Introduction In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied [...]

Abstract Energy efficiency has emerged as an overarching concern due to the high pollution and cost associated with operating heating, ventilation and air-conditioning systems in buildings, which are an essential part of our day to day life. Besides, energy monitoring becomes one of the most important research topics nowadays as it enables us the possibility of understanding the consumption of the facilities. This, along with energy forecasting, represents a very decisive task for energy efficiency. The goal of this study is divided into two parts. First to provide a methodology to predict energy usage every hour. To do so, several Machine Learning technologies were analysed: Trees, Support Vector Machines and Neural Networks. Besides, as the University of Granada lacks a tool to properly monitoring those data, a second aim is to propose an intelligent system to visualize and to use those models in order to predict energy consumption in real-time. To this end, we designed VIMOEN (VIsual MOnitoring of ENergy), a web-based application to provide not only visual information about the energy consumption of a set of geographically-distributed buildings but also expected expenditures in the near future. The system has been designed to be easy-to-use and intuitive for non-expert users. Our system was validated on data coming from buildings of the UGR and the experiments show that the Elman Neural Networks proved to be the most accurate and stable model and since the 5th hour the results maintain accuracy.