Residential Load Patterns Research Articles

Enhancing peak prediction in residential load forecasting with soft dynamic time wrapping loss functions

Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean squared error (MSE) or mean absolute error (MAE) for neural networks. These loss functions, while effective in overall prediction accuracy, lack specialized focus on accurately predicting load peaks. This article presents a comparative analysis of soft-DTW loss function, a smoothed formulation of Dynamic Time Wrapping (DTW), compared to other commonly used loss functions, in order to assess its effectiveness in improving peak prediction accuracy. To evaluate peak performance, we introduce a novel evaluation methodology using confusion matrix and propose new errors for peak position and peak load, tailored specifically for assessing peak performance in short-term load forecasting. Our results demonstrate the superiority of soft-DTW in capturing and predicting load peaks, surpassing other commonly used loss functions. Furthermore, the combination of soft-DTW with other loss functions, such as soft-DTW + MSE, soft-DTW + MAE, and soft-DTW + TDI (Time Distortion Index), also enhances peak prediction. However, the differences between these combined soft-DTW loss functions are not substantial. These findings highlight the significance of utilizing specialized loss functions, like soft-DTW, to improve peak prediction accuracy in short-term load forecasting.

Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method

Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method

Incremental segmented slope residential load pattern clustering based on three-stage curve profiles

ABSTRACT This paper tackles high computational complexity in using Euclidean distance for residential load profiles (RLPs) similarity by proposing a three-stage incremental segmented slope clustering framework. The first two stages involve static clustering, where we obtain typical residential load profiles through piecewise slope clustering. In the third stage, dynamic clustering is performed based on the slope similarity of RLPs. This method enhances clustering performance and reduces computation cost, outperforming various benchmarks, with simulation results confirming the framework's effectiveness.

Implementing Very-Short-Term Forecasting of Residential Load Demand Using a Deep Neural Network Architecture

The need for and interest in very-short-term load forecasting (VSTLF) is increasing and important for goals such as energy pricing markets. There is greater challenge in predicting load consumption for residential-load-type data, which is highly variable in nature and does not form visible patterns present in aggregated nodal-type load data. Previous works have used methods such as LSTM and CNN for VSTLF; however, the use of DNN has yet to be investigated. Furthermore, DNNs have been effectively used in STLF but have not been applied to very-short-term time frames. In this work, a deep network architecture is proposed and applied to very-short-term forecasting of residential load patterns that exhibit high variability and abrupt changes. The method extends previous work by including delayed load demand as an input, as well as working for 1 min data resolution. The deep model is trained on the load demand data of selected days—one, two, and a week—prior to the targeted day. Test results on real-world residential load patterns encompassing a set of 32 days (a sample from different seasons and special days) exhibit the efficiency of the deep network in providing high-accuracy residential forecasts, as measured with three different error metrics, namely MSE, RMSE, and MAPE. On average, MSE and RMSE are lower than 0.51 kW and 0.69 kW, and MAPE lower than 0.51%.

Characterizing residential load patterns on multi-time scales utilizing LSTM autoencoder and electricity consumption data

• We propose a novel framework to characterize residential load patterns utilizing LSTM-AE and electricity consumption data. • LSTM-AE model is designed for dimensionality reduction and feature extraction. • Two-level clustering method is proposed to discover TLPs and MLPs on multi-time scales. • TLPs profile resident's load patterns from a global view, MLPs characterize load patterns of individual and groups. • Consumer groups and residential energy lifestyles are revealed to customize personal demand response strategies. Load patterns represent a clear picture of electricity usage, reflecting the consumer's habits. Previous works mainly focused on load patterns discovery on a fixed scale, but limited to characterize load patterns on multi-time scales utilizing electricity consumption data (ECD). Therefore, we propose a novel framework to characterize residential load patterns on multi-time scales. The long-short-term memory autoencoder (LSTM-AE) model is designed for dimensionality reduction and feature extraction. Furthermore, a two-level clustering method is proposed to discover and characterize typical load patterns (TLPs) and multifaceted load patterns (MLPs) on multi-time scales. The proposed framework is comprehensively evaluated via extensive experiments on three real ECD. Results show that: (1) Reconstruction errors of LSTM-AE are lower than 6 benchmark models across different time scales, which validates the superiority of LSTM-AE. (2) TLPs and MLPs on daily, weekly, monthly and yearly scale are discovered by the two-level clustering method. TLPs profile the resident's electricity usages from a global view. (3) MLPs present the consumer segmentation and characterize residential load patterns of individual and groups. Especially, customer groups and electricity usage habits or lifestyles are revealed thoroughly to customize personal demand response strategies. This study can provide new valuable insights for smart grid applications.

Meta-Heuristic Optimization Based Two-stage Residential Load Pattern Clustering Approach Considering Intracluster Compactness and Inter-cluster Separation

This article proposes a meta-heuristic optimization-based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction; 2) a good clustering should achieve a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach.

Comparing smart metered, residential power demand with standard load profiles

Abstract Despite their age, residential standard load profiles (SLPs) are a widely used tool to represent the electric power demand of a household. However, the prospect of a future power system relying mainly on renewable and often intermittent, distributed energy resources (DER) is pushing SLPs to their limit. Determining self consumption rates and the potential of demand response through smart home applications or simulating distribution grids, especially the low voltage (LV) level—all require a realistic replication of residential load patterns. However, data of high temporal resolution for individual households is rare. Therefore, several smart meters were installed at participants’ homes within a research project and the measurements are analyzed. For the first time, complete load profiles for a whole year in a temporal resolution of 15 s are used to derive key characteristics. For example, the level of fluctuation is described by different quantiles of the instantaneous power. It is found that the 99% quantile exceeds mean power values by 5 – 10 times, but pooling of households reduces this factor strongly. Other findings, for example that one third to half of the energy is consumed at low power levels (

Residential Load Pattern Analysis for Smart Grid Applications Based on Audio Feature EEUPC

The smart grid is an important application field of the Internet of things. This paper presents a method of user electricity consumption pattern analysis for smart grid applications based on the audio feature EEUPC. A novel similarity function based on EEUPC is adapted to support clustering analysis of residential load patterns. The EEUPC similarity exploits features of peaks and valleys on curves instead of directly comparing values and obtains better performance for clustering analysis. Moreover, the proposed approach performs load pattern clustering, extracts a typical pattern for each cluster, and gives suggestions toward better power consumption for each typical pattern. Experimental results demonstrate that the EEUPC similarity is more consistent with human judgment than the Euclidean distance and higher clustering performance can be achieved for residential electric load data.

Probabilistic characterisation of the aggregated residential load patterns

The electrical load pattern representing the residential consumption is subject to different types of uncertainty, depending on family composition, lifestyle, number and type of use of the electrical appliances, thus requiring a dedicated analysis within a probabilistic framework. A comprehensive approach to the probabilistic characterisation of aggregated residential consumers supplied by the same feeder or substation, from the point of view of the electricity supplier, is presented. Starting from the result of a statistical study carried out on single-house extra-urban residential consumers, the time evolution of the average value and the standard deviation of the aggregated load power are presented. In addition, a dedicated goodness-of-fit analysis has been carried out to identify the most suitable probability distributions representing the daily aggregated load pattern at different times, referred to a set of typical days. This analysis includes a novel characterisation of the critical error of the Kolmogorov-Smirnov test on various probability distributions (beta, gamma, Gumbel, log-normal, Rayleigh and Weibull) for which the critical errors are not specifically available in publications. The gamma and log-normal probability distributions have emerged as the most promising solutions for several time intervals of analysis and for different numbers of aggregated consumers.