A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

Abstract

The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

“…Bekdaş et al [10] utilized five foundational regression algorithms and five ensemble algorithms to predict cooling loads (the amount of energy that must be removed from or consumed in a space to keep its temperature at an acceptable level or within an acceptable range) based on the basic architectural and structural characteristics of low-rise buildings in the tropics and found that the Histogram Gradient Boosting algorithm and stacking models efficiently modeled the relationship between the predictors and cooling load. Matos et al [11] suggested a method to manage community energy balance through electricity consumption forecasts via eXtreme Gradient Boosting (XGBoost) and used a decision algorithm for energy trading with the public grid based on solar production and energy consumption forecasts, storage levels, and market electricity prices. Son et al [12] suggested an algorithm for short-and medium-term electricity consumption forecasts by combining the Gated Recurrent Unit (GRU) model (which achieves accurate long-term forecasts) with the Prophet model (which is appropriate for modeling seasonal events), and their proposed model reduced forecasting errors and provided insights into electricity consumption patterns.…”