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.
Highlights
Introduction published maps and institutional affilThe different kinds of appliances have increased significantly in households, and the residential electrical load has maintained a medium–high growth rate over the years
Short-term residential load forecasting is the precondition of the day-ahead and intraday scheduling strategy of the household microgrid
50 households are predicted with a mean absolute percentage error (MAPE) of 14.3% and 100 households are predicted with a MAPE of 10.2%
Summary
The residential electric load consists of different home appliances. These residential electric loads can be grouped into three categories. The first kind of load works at a relatively consistent time every day, including appliances such as rice cookers, kitchen ventilators, and refrigerators. The second kind of load is directly determined by external factors, including heating, air-conditioning, and electric fans. The third kind of load would work every day, but the corresponding operation time would vary and be influenced by the routine life of the host family, including the electric water heater and laundry machine. The sampling intervals of residential load are in the range of 15 min to one hour in the field [38]. The historical data of residential electric load would be analyzed to obtain the statistical characteristics based on typical quantitative indicators
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