Accurate short-term load forecasting (STLF) is required for reliable power system operations. Nevertheless, load forecasting remains a challenge owing to the high dimensionality and volatility of electrical load data as time series. In this study, a feature extraction framework for electrical load and other complementary data as a multivariate time series is proposed. The proposed framework consists of tagging and embedding processes that extract patterns from the multivariate time series as tags and capture their temporal and dimensional relations. In the embedding process, a network model that embeds the tags is deliberately designed with a convolutional layer in a multi-output structure based on mathematical analysis. Furthermore, a deep learning-based STLF model is constructed with the proposed feature extraction framework. The performance of the proposed STLF model for day-ahead load forecasting is evaluated on a publicly available set of real electricity demand data. The experimental results verify that the proposed approach reduces the root mean squared error by 5% to 12%. This improvement in load forecasts can benefit power grid operations as it provides more accurate expectations on the behaviors of the power grid in short term, which can be utilized in power grid applications, such as power dispatch and scheduling.

This paper studies distributed computing mechanisms on heterogeneous mobile devices (MDs) for latency reduction in Internet of Things (IoT) services by mitigating the effect of straggling MDs. We propose novel coded distributed computing mechanisms with two different incentive distributions that consider the time-discounting value of processed results and the amount of the workload computed by MDs. Specifically, we consider distributed gradient descent computing with coding when a task publisher (TP) with a limited amount of budget offers incentives to encourage MDs’ participation in the computation. To analyze a hierarchical decision-making structure of the TP and MDs, we formulate a strategic competition between them as a Stackelberg game. In the case that the MDs are the leaders, we design a CPU-cycle frequency control scheme to balance each MD’s computing speed and energy consumption for obtaining its maximum utility with the incentive mechanisms. As the follower, the TP aims at minimizing latency of the distributed computing, and it follows the MDs’ decisions to determine the load allocation for each MD. We, then, design an algorithm achieving the Stackelberg equilibrium, which is shown to be a unique Nash equilibrium of the game. The performance evaluation results show that the proposed mechanisms achieve 39% of latency reduction on average compared to the benchmark mechanism. Furthermore, the results corroborate the efficiency of the proposed mechanisms in terms of the MDs’ social welfare.

In this paper, a clustering method with proposed distance measurement to extract base load profiles from arbitrary data sets is studied. Recently, smart energy load metering devices are broadly deployed, and an immense volume of data is now collected. However, as this large amount of data has been explosively generated over such a short period of time, the collected data is hardly organized to be employed for study, applications, services, and systems. This paper provides a foundation method to extract base load profiles that can be utilized by power engineers, energy system operators, and researchers for deeper analysis and more advanced technologies. The base load profiles allow them to understand the patterns residing in the load data to discover the greater value. Up to this day, experts with domain knowledge often have done the base load profile realization manually. However, the volume of the data is growing too fast to handle it with the conventional approach. Accordingly, an automated yet precise method to recognize and extract the base power load profiles is studied in this paper. For base load profile extraction, this paper proposes Sample Pearson Correlation Coefficient (SPCC) distance measurement and applies it to Mean-Shift algorithm based nonparametric mode-seeking clustering. The superiority of SPCC distance over traditional Euclidean distance is validated by mathematical and numerical analysis.

This paper proposes a building energy management model in the Korean electric tariff system with the consideration of prediction errors in load consumption and renewable energy generation. The increase in controllable power resources has enabled demand-side managements in the practical environment, and various approaches to optimize power consumption in the building are studied and implemented. However, demand side management has a problem in that it is difficult for power consumers to control power consumption due to the lack of consideration of actual use environment. To resolve this issue, we propose penalty factor to minimize the gap between power scheduling results and usual power usage patterns. In addition, the prediction error of power consumption in real environment causes difficulty in power operation. To resolve the issue, this paper proposes a method to control power demand resources and energy storage system based on a set of reserve portion. The proposed model includes analysis on prediction errors of power data, and set of proper reserve proportion in demand response and energy storage system operation. The simulation results show that the electricity rate of consumers in the building is reduced by more than 21% per month.