A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms

Abstract

Short-term residential load forecasting is the precondition of the day-ahead and intra-day scheduling strategy of the household microgrid. Existing short-term electric load forecasting methods are mainly used to obtain regional power load for system-level power dispatch. Due to the high volatility, strong randomness, and weak regularity of the residential load of a single household, the mean absolute percentage error (MAPE) of the traditional methods forecasting results would be too big to be used for home energy management. With the increase in the total number of households, the aggregated load becomes more and more stable, and the cyclical pattern of the aggregated load becomes more and more distinct. In the meantime, the maximum daily load does not increase linearly with the increase in households in a small area. Therefore, in our proposed short-term residential load forecasting method, an optimal number of households would be selected adaptively, and the total aggregated residential load of the selected households is used for load prediction. In addition, ordering points to identify the clustering structure (OPTICS) algorithm are also selected to cluster households with similar power consumption patterns adaptively. It can be used to enhance the periodic regularity of the aggregated load in alternative. The aggregated residential load and encoded external factors are then used to predict the load in the next half an hour. The long short-term memory (LSTM) deep learning algorithm is used in the prediction because of its inherited ability to maintain historical data regularity in the forecasting process. The experimental data have verified the effectiveness and accuracy of our proposed method.

Entropy-Based Anomaly Detection in Household Electricity Consumption

“…The approaches presented in [28,31] combine convolutional neural network (CNN) and LSTM, which outperform existing approaches. However, there are recent works that show that the LSTM model can obtain good accuracy in this area [32][33][34].…”

Comparative Analysis of Residential Load Forecasting with Different Levels of Aggregation

“…A methodology for demand forecasting of 200 consumers that are divided into groups of 50, 100, and 150 is presented in [25]. Forecasting is performed by LSTM and k-means clustering.…”

Residential load forecasting based on electricity consumption pattern clustering

“…Residential electricity consumption behavior, especially related to the high-energy-consuming equipment, can be changed for participation in DR, which leads to extra fluctuations in residential load. Except for the influence of DR, different energy-use habits result in a large difference in residential load power and relatively weak regularity (Lusis et al, 2017), which makes nowadays residential load forecasting more challenging (Hou et al, 2021;Liu et al, 2023).…”

“…The K-means clustering algorithm is introduced to cluster the load and the load is predicted based on deep learning. Evidence shows that under the premise of using the same algorithm, the accuracy of the prediction after clustering is generally higher than that of direct prediction (Liu et al, 2021). However, since these clustering algorithms manually select the number of clustering centers, these clustering methods cannot reflect sufficient information on residential load, which might lead to inaccurate clustering results.…”