A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series

Abstract

Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model.

Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

“…The paper reveals that LSTM outperforms conventional backpropagation ANNs, since it is much more able to learn long-term temporal correlations. Another recent approach based on LSTM is [43]. The proposed solution is very flexible: it is able to forecast energy consumption with good accuracy, also when the LSTM model is used for load prediction of residential houses whose historical samples have not been included in the training set.…”

“…The model adopted in this work consists of four layers (two hidden) and is similar to the one adopted in [43]:…”

“…2. The model structure is analogous to the one described in Section VI-A and includes four layers, as done in the state-of-the-art work [43], which embraces a centralized architecture and is thus considered as benchmark: an input layer, two LSTM hidden layers including 32 and 16 LSTM cells respectively, and an output dense layer with one output unit. All the hyperparameters were chosen as specified in Section VI-B with only two differences:…”

“…2 and 4. As specified in Section VI-C, the state-of-the art centralized architecture considered for comparison is the one proposed in [43].…”

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

“…Considering the increase in the number of smart metering equipment and infrastructure throughout the world, an increase that has led to huge volumes of useful data related to electricity consumption and disaggregated loads, in [12], Alonso et al introduce a processing and forecasting methodology for multiple time series datasets recorded by means of smart-metering devices. In contrast to classical and univariate methods, the authors put forward a RNN model with LSTM for the purpose of identifying different patterns of electricity consumption stemming from different individual electricity consumers and households, with the goal of achieving day-ahead accurate predictions while consuming very low computational resources.…”