Search

Accurate and interpretable short-term load forecasting tasks are essential to the optimal operation of liberalized electricity markets since they contribute to the efficient development of energy trading and demand response strategies as well as the successful integration of renewable energy sources. Consequently, performant day-ahead consumption forecasting models need to capture feature nonlinearities, analyze system dynamics and conserve evolving temporal patterns in order to minimize the impact of noise and adapt to concept drift. Prominent estimators and standalone decomposition-based approaches may not fully address those challenges as they often yield small error rate improvements and omit optimal time series evolution. Therefore, in this work we propose a combinatorial component decomposition method focused on the selection of important renewable generation component sequences extracted from the combined output of seasonal-trend decomposition using locally estimated scatterplot smoothing, singular spectrum analysis and empirical mode decomposition methods. The proposed method was applied on five well-known kernel models in order to evaluate day-ahead consumption forecasts on linear, tree-based and neural network structures. Moreover, for the assessment of pattern conservation, an intuitive metric function, labeled as Weighted Average Unanchored Chain Divergence (WAUCD), based on distance scores and unanchored time series chains is introduced. The results indicated that the application of the combinatorial component method improved the accuracy and the pattern conservation capabilities of most models substantially. In this examination, the long short-term memory (LSTM) and deep neural network (DNN) kernels reduced their mean absolute percentage error by 46.87% and 42.76% respectively and predicted sequences that consistently evolved over 30% closer to the original target in terms of daily and weekly patterns. [ABSTRACT FROM AUTHOR]

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field. [ABSTRACT FROM AUTHOR]

The evolution of electricity markets has led to increasingly complex energy trading dynamics and the integration of renewable energy sources as well as the influence of several external market factors contributed towards price volatility. Therefore, day-ahead electricity price forecasting models, typically using some kind of neural network, play a crucial role in the optimal behavior of market agents. The most prominent models and benchmarks rely on improving the accuracy of predictions and the time for convergence by some sort of a priori processing of the dataset that is used for the training of the neural network, such as hyperparameter tuning and feature selection techniques. What has been overlooked so far is the possible benefit of a posteriori processing, which would consider the effects of parameters that could refine the predictions once they have been made. Such a parameter is the estimation of the residual training error. In this study, we investigate the effect of residual training error estimation for the day-ahead price forecasting task and propose an error compensation deep neural network model (ERC–DNN) that focuses on the minimization of prediction error, while reinforcing error stability through the integration of an autoregression module. The experiments on the Nord Pool power market indicated that this approach yields improved error metrics when compared to the baseline deep learning structure in different training scenarios, and the refined predictions for each hourly sequence shared a more stable error profile. The proposed method contributes towards the development of more flexible hybrid neural network models and the potential integration of the error estimation module in future benchmarks, given a small and interpretable set of hyperparameters. [ABSTRACT FROM AUTHOR]

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange between the main grid and the RES. [ABSTRACT FROM AUTHOR]

Hybrid Renewable Energy Systems (HRES) are an attractive solution for the supply of electricity in remote areas like islands and communities where grid extension is difficult. Hybrid systems combine renewable energy sources with conventional units and battery storage in order to provide energy in an off-grid or on-grid system. The purpose of this study is to examine the techno-economical feasibility and viability of a hybrid system in Donoussa island, Greece, in different scenarios. A techno-economic analysis was conducted for a hybrid renewable energy system in three scenarios with different percentages of adoption rate (20%, 50% and 100%)and with different system configurations. Using HOMER Pro software the optimal system configuration between the feasible configurations of each scenario was selected, based on lowest Net Present Cost (NPC), minimum Excess Electricity percentage, and Levelized Cost of Energy (LCoE). The results obtained by the simulation could offer some operational references for a practical hybrid system in Donoussa island. The simulation results confirm the application of a hybrid system with 0% of Excess Electricity, reasonable NPC and LCoE and a decent amount of renewable integration. [ABSTRACT FROM AUTHOR]