Residential Power Forecasting Using Load Identification and Graph Spectral Clustering

Forecasting energy or power usage is an important part of providing a stable supply of power to all customers on a power grid. We present a novel method that aims to forecast the power consumption of a single house, or a set of houses, based on non-intrusive load monitoring (NILM) and graph spectral clustering. In the proposed method, the aggregate power signal is decomposed into individual appliance signals and each appliance's power is forecasted separately. Then the total power forecast is formed by aggregating forecasted power levels of individual appliances. We use four publicly available datasets (reference energy disaggregation dataset, rainforest automation energy, almanac of minutely power dataset version 2, tracebase) to test our forecasting method and report its accuracy. The results show that our method is more accurate compared to popular existing approaches, such as autoregressive integrated moving average, similar profile load forecast, artificial neural network, and recent NILM-based forecasting.

I. Introduction

Accurate power demand forecasting is important for maintaining a stable power grid. With advance warning of demand surges, energy providers would be able to better plan their power generation and/or perform other measures such as peak shaving or load shifting [1], [2]. Various forecasting methods have been proposed based on extrapolation [3], [4], Kalman filtering [5]–[7], fuzzy logic [6], [8]–[10], autoregressive integrated moving average (ARIMA) models [11]–[14], artificial neural networks (ANN) [6], [11], [15], and similar profiles load forecast (SPLF) [16], [17]. All these methods attempt to forecast the aggregate power (all loads combined) directly by relying on the temporal dependence of the aggregate power signal. However, we note that stronger temporal dependence may exist in power signals of individual appliances. This is easily seen in the case of cyclical appliances such as refrigerators, which turn ON and OFF roughly periodically. When the power signals of different appliances are aggregated, such temporal dependence may be disrupted, hence forecasting the aggregate power may be more difficult than forecasting the power usage of individual appliances.