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

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.

Demand Side Management Technique to Optimally Schedule Electric Vehicle Loads in Smart Grid

Demand-side management (DSM) in smart grids helps the problem of reducing peak load of utilities during certain hourly periods. Based on DSM techniques, peak load hours can be equalized to non-peak load hours therefore users will have less bill payments. In this paper optimal scheduling of Electric Vehicles (EVs) is done based on an objective function formulated to minimize the load variations. Firstly, hourly consumption of load during a day at Koneru Lakshmaiah Education Foundation is considered, EVs load is assumed and flattened the aggregated load curve by optimally scheduling the EVs during off peak hours.