The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system.

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.

Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. The introduction of smart control systems capable of handling additional fuzzy parameters, such as weather data, through machine learning methods, offers valuable insights in an attempt to adjust consumer behavior optimally. Following recent advances in the field of fuzzy control, this study presents the design and implementation of a fuzzy control system that processes environmental data in order to recommend minimum energy consumption values for a residential building. This system follows the forward chaining Mamdani approach and uses decision tree linearization for rule generation. Additionally, a hybrid feature selector is implemented based on XGBoost and decision tree metrics for feature importance. The proposed structure discovers and generates a small set of fuzzy rules that highlights the energy consumption behavior of the building based on time-series data of past operation. The response of the fuzzy system based on sample input data is presented, and the evaluation of its performance shows that the rule base generation is derived with improved accuracy. In addition, an overall smaller set of rules is generated, and the computation is faster compared to the baseline decision tree configuration.

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.

The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting (STLF) is critical for the daily operation of power plants, and state-of-the-art approaches for it involve hybrid models that deploy regressive deep learning algorithms, such as neural networks, in conjunction with clustering techniques for the pre-processing of load data before they are fed to the neural network. This paper develops and evaluates four robust STLF models based on Multi-Layer Perceptrons (MLPs) coupled with the K-Means and Fuzzy C-Means clustering algorithms. The first set of two models cluster the data before feeding it to the MLPs, and are directly comparable to similar existing approaches, yielding, however, better forecasting accuracy. They also serve as a common reference point for the evaluation of the second set of two models, which further enhance the input to the MLP by informing it explicitly with clustering information, which is a novel feature. All four models are designed, tested and evaluated using data from the Greek power system, although their development is generic and they could, in principle, be applied to any power system. The results obtained by the four models are compared to those of other STLF methods, using objective metrics, and the accuracy obtained, as well as convergence time, is in most cases improved.

In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.