Past Vector Similarity for Short Term Electrical Load Forecasting at the Individual Household Level

Abstract

Demand side management (DSM) programs are an integral part of the modern grid. Most of these DSM programs are designed to work at household and hour level. The optimization problems in these DSM programs are guided by the forecasted load. An error in the hour ahead load forecasting results in a suboptimal solution entailing economic cost to both the utility and the customers. Predicting loads at a fine granularity (e.g., households) is challenging due to a large number of (known or unknown) factors affecting power consumption. At larger scales (e.g., clusters of consumers), since the inherent stochasticity and fluctuations are averaged out, the problem becomes substantially easier. Many techniques have been proposed to predict loads of clusters of consumers in various localities with great accuracy. Also, these techniques generally utilize sociological and weather information and work better on data from a particular locality. In this paper, a new technique called Past Vector Similarity (PVS) has been proposed to predict electricity load one hour ahead at the level of an individual household. The proposed strategy is based on load information only and does not require calendar, weather or any other attributes. In fact, the idea is to extract the exact load patterns of individual households corresponding to their routine and socioeconomic values. Consequently, the technique makes use of the recent past vector and generate similar patterns for the prediction of future load profiles. Furthermore, the ensemble of these similar loads is an efficient prediction of electricity. PVS has just two parameters due to which it can be applied to the smaller dataset without overfitting issue. Moreover, due to the parallel nature of PVS, it can be scaled for a large number of customers without computation burden. The proposed PVS has been assessed empirically for two distinct datasets from Australia and Sweden. The simulation results demonstrate that the PVS significantly outperforms other state-of-the-art forecasting methods in terms of accuracy.

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

“…A prioritized experience replay automated RL To provide a coupled approach with multi period forecasting and DR program. [64] Hybrid network consisted the layers of autoencoder LSTM, bidirectional LSTM, and stack of LSTM To showcase the superior performance of the proposed hybrid model when tested with a dataset collected from a residential home, in comparison to previous studies with similar objectives.…”

“…The proposed hybrid model was applied to the power consumption dataset to predict the three targets independently. The hyperparameter search space given to the models includes the following settings: 'learning rate': [0.1, 0.01, 0.001], 'dropout rate': [0.2, 0.3, 0.4], 'epochs': [50,100,150], 'batch size': [32,64,128], and 'activation': ['relu', 'sigmoid']. The optimal parameters that were determined for each experiment are presented in Table 7.…”

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

“…In short‐term electrical load forecasting at the individual household level, the ARIMA method has a mean absolute percentage error (MAPE) of 199.6% and random forest (RF) has a MAPE of 234.2% 26 . Extreme learning machine (ELM) has a MAPE of 136.5% for short‐term load forecasting, based on five different energy load datasets 27 .…”

“…25 In short-term electrical load forecasting at the individual household level, the ARIMA method has a mean absolute percentage error (MAPE) of 199.6% and random forest (RF) has a MAPE of 234.2%. 26 Extreme learning machine (ELM) has a MAPE of 136.5% for short-term load forecasting, based on five different energy load datasets. 27 A review of the literature shows that the MAPE values of shallow learning for energy forecasting often exceed 100%, indicating that the predictive values deviate greatly from the actual values so shallow learning is ineffective for forecasting energy use.…”