Jesus Lago

Long-term individual household forecasting is useful in various applications, e.g., to determine customers’ advance payments. However, the literature on this type of forecasting is limited; existing methods either focus on short-term predictions for individual households, or long-term prediction at an aggregated level (e.g., neighborhood). To fill this gap, we present a method that predicts the monthly consumption of individual households over the next year, given only a few months of consumption data during the current year. Utility companies can exploit this method to predict the consumption of any customer for the next year even with incomplete data. The method consists of three steps: clustering the data using ${k}$ -means, prediction using an ensemble of forecasts based on the historical median distribution among similar households, and smoothing the predictions to remove weather-dependent patterns. The method is highly accurate as it finished third in the IEEE-CIS competition (and ranks first when leveraging insights from another team), focused on forecasting long-term household consumption with incomplete data. It is also very scalable thanks to its low computational complexity and weak data requirements: the method only requires a few months of historical data and no household-specific or weather information.

While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by performing a literature survey of state-of-the-art models, comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies.

Seasonal thermal energy storage systems (STESSs) can shift the delivery of renewable energy sources and mitigate their uncertainty problems. However, to maximize the operational profit of STESSs and ensure their long-term profitability, control strategies that allow them to trade on wholesale electricity markets are required. While control strategies for STESSs have been proposed before, none of them addressed electricity market interaction and trading. In particular, due to the seasonal nature of STESSs, accounting for the long-term uncertainty in electricity prices has been very challenging. In this article, we develop the first control algorithms to control STESSs when interacting with different wholesale electricity markets. As different control solutions have different merits, we propose solutions based on model predictive control and solutions based on reinforcement learning. We show that this is critical since different markets require different control strategies: MPC strategies are better for day-ahead markets due to the flexibility of MPC, whereas reinforcement learning (RL) strategies are better for real-time markets because of fast computation times and better risk modeling. To study the proposed algorithms in a real-life setup, we consider a real STESS interacting with the day-ahead and imbalance markets in The Netherlands and Belgium. Based on the obtained results, we show that: 1) the developed controllers successfully maximize the profits of STESSs due to market trading and 2) the developed control strategies make STESSs important players in the energy transition: by optimally controlling STESSs and reacting to imbalances, STESSs help to reduce grid imbalances.