Regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data

Heterogeneity is a hallmark of complex diseases. Regression-based heterogeneity analysis, which is directly concerned with outcome-feature relationships, has led to a deeper understanding of disease biology. Such an analysis identifies the underlying subgroup structure and estimates the subgroup-specific regression coefficients. However, most of the existing regression-based heterogeneity analyses can only address disjoint subgroups; that is, each sample is assigned to only one subgroup. In reality, some samples have multiple labels, for example, many genes have several biological functions, and some cells of pure cell types transition into other types over time, which suggest that their outcome-feature relationships (regression coefficients) can be a mixture of relationships in more than one subgroups, and as a result, the disjoint subgrouping results can be unsatisfactory. To this end, we develop a novel approach to regression-based heterogeneity analysis, which takes into account possible overlaps between subgroups and high data dimensions. A subgroup membership vector is introduced for each sample, which is combined with a loss function. Considering the lack of information arising from small sample sizes, an $l_2$ norm penalty is developed for each membership vector to encourage similarity in its elements. A sparse penalization is also applied for regularized estimation and feature selection. Extensive simulations demonstrate its superiority over direct competitors. The analysis of Cancer Cell Line Encyclopedia data and lung cancer data from The Cancer Genome Atlas shows that the proposed approach can identify an overlapping subgroup structure with favorable performance in prediction and stability.Comment: 33 pages, 16 figure

Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting

Electric load forecasting is an indispensable component of electric power system planning and management. Inaccurate load forecasting may lead to the threat of outages or a waste of energy. Accurate electric load forecasting is challenging when there is limited data or even no data, such as load forecasting in holiday, or under extreme weather conditions. As high-stakes decision-making usually follows after load forecasting, model interpretability is crucial for the adoption of forecasting models. In this paper, we propose an interactive GAM which is not only interpretable but also can incorporate specific domain knowledge in electric power industry for improved performance. This boosting-based GAM leverages piecewise linear functions and can be learned through our efficient algorithm. In both public benchmark and electricity datasets, our interactive GAM outperforms current state-of-the-art methods and demonstrates good generalization ability in the cases of extreme weather events. We launched a user-friendly web-based tool based on interactive GAM and already incorporated it into our eForecaster product, a unified AI platform for electricity forecasting

Effects of Mountain Rivers Cascade Hydropower Stations on Water Ecosystems

China is rich in hydropower resources, and mountain rivers have abundant water resources and huge development potential, which have a profound impact on the pattern of water resources allocation in China. As the main way of water resources and hydropower development, the construction of cascade hydropower stations, while meeting the requirements of water resources utilization for social development, has also brought adverse effects on river ecosystems. Therefore, the impact of the construction of cascade hydropower stations on mountainous river ecosystems, where the minimum ecological flow of rivers must be ensured and reviewed. In addition, this paper proposed the deficiencies and outlooks for cascade hydropower stations based on previous research results

Implication of Production Tax Credit on Economic Dispatch for Electricity Merchants with Storage and Wind Farms

The production tax credit (PTC) promotes wind energy development, reduces power generation costs, and can affect merchants\u27 joint economic dispatch, particularly for electricity merchants with both energy storage and wind farms. Two common PTC policies are studied – in the first policy, a wind farm receives PTC by selling wind generation to the market and its storage can be used to store energy from the wind generation and energy purchased from the grid but the energy released from the storage cannot receive PTC; in the second policy, the energy released from the storage can also qualify for PTC but purchasing energy from the grid is not allowed. We then employ dynamic programming to study merchants\u27 optimal decision-making while considering PTC and the physical characteristics of storage systems. We analytically show that the state of charge (SOC) range can be segmented into different regions by SOC reference points under two PTC policies. The merchant\u27s optimal action can be conveniently and uniquely determined based on the region within which the current SOC falls. Moreover, this study illustrates that PTC could substantially alter the optimal scheduling policy structures by affecting reference points and their relationships. The results showed that the frequencies for charging and discharging storage decisions decreased with an increase in PTC subsidy. Last, we confirm that, although the first policy allows merchants to buy electricity from the market, the second policy can bring more profits when the PTC is large at the current PTC rates. The findings can provide multistage decision-making guidance to electricity merchants in the wholesale power market

A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway

Congestion occurs and propagates in the stations of urban rail transit, which results in the impendent need to comprehensively evaluate the station performance. Based on complex network theory, a key station identification method is considered. This approach considers both the topology and dynamic operation states of urban rail transit network, such as degree, passenger demand, system capacity and capacity utilization. A case of Beijing urban rail transit is applied to verify the validation of the proposed method. It shows that the method can be helpful to daily passenger flow control and capacity enhancement during peak hours.</p