Forecasting day-ahead electricity prices in Europe: the importance of considering market integration

Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Improved Heat Demand Prediction of Individual Households

One of the options to increase the energy efficiency of current electricity network is the use of a Virtual Power Plant. By using multiple small (micro)generators distributed over the country, electricity can be produced more efficiently since these small generators are more efficient and located where the energy is needed. In this paper we focus on micro Combined Heat and Power generators. For such generators, the production capacity is determined and limited by the heat demand. To keep the global electricity network stable, information about the production capacity of the heat-driven generators is required in advance. In this paper we present methods to perform heat demand prediction of individual households based on neural network techniques. Using different input sets and a so called sliding window, the quality of the predictions can be improved significantly. Simulations show that these improvements have a positive impact on controlling the distributed microgenerators

Using heat demand prediction to optimise Virtual Power Plant production capacity

In the coming decade a strong trend towards distributed electricity generation (microgeneration) is expected. Micro-generators are small appliances that generate electricity (and heat) at the kilowatt level, which allows them to be installed in households. By combining a group of micro-generators, a Virtual Power Plant can be formed. The electricity market/network requires a VPP control system to be fast, scalable and reliable. It should be able to adjust the production quickly, handle in the order of millions of micro-generators and it should ensure the required production is really produced by the fleet of microgenerators. When using micro Combined Heat and Power microgenerators, the electricity production is determined by heat demand. In this paper we propose a VPP control system design using learning systems to maximise the economical benefits of the microCHP appliances. Furthermore, ways to test our design are\ud described