AI-Powered Predictions for Electricity Load in Prosumer Communities

TL;DR

This paper reviews and tests AI-powered short-term load forecasting methods for prosumer communities, demonstrating that combining persistent and regression models yields the most accurate electricity load predictions.

Contribution

It introduces and evaluates various AI and statistical load forecasting techniques, highlighting the effectiveness of combining persistent and regression models for prosumer community energy management.

Findings

Combining persistent and regression models improves forecast accuracy.

AI models like Prophet and LSTM perform well in load prediction.

Weather data integration enhances forecasting performance.

Abstract

The flexibility in electricity consumption and production in communities of residential buildings, including those with renewable energy sources and energy storage (a.k.a., prosumers), can effectively be utilized through the advancement of short-term demand response mechanisms. It is known that flexibility can further be increased if demand response is performed at the level of communities of prosumers, since aggregated groups can better coordinate electricity consumption. However, the effectiveness of such short-term optimization is highly dependent on the accuracy of electricity load forecasts both for each building as well as for the whole community. Structural variations in the electricity load profile can be associated with different exogenous factors, such as weather conditions, calendar information and day of the week, as well as user behavior. In this paper, we review a wide range of electricity load forecasting techniques, that can provide significant assistance in optimizing load consumption in prosumer communities. We present and test artificial intelligence (AI) powered short-term load forecasting methodologies that operate with black-box time series models, such as Facebook's Prophet and Long Short-term Memory (LSTM) models; season-based SARIMA and smoothing Holt-Winters models; and empirical regression-based models that utilize domain knowledge. The integration of weather forecasts into data-driven time series forecasts is also tested. Results show that the combination of persistent and regression terms (adapted to the load forecasting task) achieves the best forecast accuracy.