Data-Driven Baseline Estimation of Residential Buildings for Demand Response

Abstract

The advent of advanced metering infrastructure (AMI) generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL) estimation in demand response (DR) management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM) and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities' residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate.

Deep Neural Network Based Demand Side Short Term Load Forecasting

“…In addition, DR is considered as one of the lease expensive resources available for operating the system according to the new paradigm under deregulation [4], and thus, forecasting the DR resource is important in the DR market. We also note that accurate demand forecasting can encourage customers to participate in DR programs and to receive monetary rewards from the utility company [5]. Meanwhile, in the deregulated retail market, various end-user services, such as customer load management, require load forecasting to attract more customers.…”

“…Other types of neural networks, such as a self-organizing map (SOM), are also used for prediction and classification [20]. In [5,21], the two-stage adaptive prediction model based on SOM and K-means clustering was used for a buildings and apartments dataset.…”

Robust Operation of Energy Storage System with Uncertain Load Profiles

“…To apply robust optimization, we transform Problem 2 into the above formulation of (10)- (12). Since l i in (5) and (9) is uncertain, we put (5) and (9) into the following equivalent matrix form as in (13):…”

“…Load forecasting, however, cannot be always accurate since it is related with unexpected exogenous inputs for operations as well as weather conditions [11]. Nevertheless, many previous works about ESS operation did not actively consider load uncertainty [12][13][14]. In this regard, we leverage robust optimization for ESS scheduling to solve the problem of load uncertainty.…”

Electricity Consumption Clustering Using Smart Meter Data

“…Several methods for clustering have been applied and the most prevalent is K-means [5,6] and derivatives such as fuzzy K-Means [7,8] and adaptive K-Means [9]. Further algorithms like hierarchical clustering [10,11], and random effect mixture models [12,13] are also popular.…”