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Abstract Greater adoption of renewable energy technologies by households is a key component of decarbonization and energy transition goals. Although existing literature has examined how sociodemographic characteristics, “green” preferences, and peer effects impact adoption of new energy technology, the role of behavioral preferences has not been adequately studied. In this paper, we examine the effect of two types of behavioral preferences, namely the degree of risk tolerance (risk preference) and attitude toward delayed reward (time preference) on the contract decision to lease or own a solar photovoltaic (PV) system. We develop a theoretical framework to show that the effect of risk and time preferences on the relative utilities from the two contracts is monotonic: Lower risk aversion and lower discount rate (more patience) imply a higher chance of solar PV ownership. To test these predictions empirically, we first estimate preference parameters (risk aversion and discount rate) from laboratory data collected from solar PV adopters. We then combine the parameter estimates with data on actual solar PV contract choice to examine the relationship between solar PV adopters' time and risk preferences and their lease‐versus‐own choice. Our regression results confirm that less risk averse individuals have a higher tendency to choose the ownership option, whereas more patient individuals are (weakly) more likely to own their solar PV systems. These findings contribute to a greater understanding of the role of behavioral factors in household decisions related to energy technologies. 

Dealing with data heterogeneity is a key challenge in the theoretical analysis of federated learning (FL) algorithms. In the literature, gradient divergence is often used as the sole metric for data heterogeneity. However, we observe that the gradient divergence cannot fully characterize the impact of the data heterogeneity in Federated Averaging (FedAvg) even for the quadratic objective functions. This limitation leads to an overestimate of the communication complexity. Motivated by this observation, we propose a new analysis framework based on the difference between the minima of the global objective function and the minima of the local objective functions. Using the new framework, we derive a tighter convergence upper bound for heterogeneous quadratic objective functions. The theoretical results reveal new insights into the impact of the data heterogeneity on the convergence of FedAvg and provide a deeper understanding of the two-stage learning rates. Experimental results using non-IID data partitions validate the theoretical findings. 

In this work, we develop a two time-scale deep learning approach for beamforming and phase shift (BF-PS) design in time-varying RIS-aided networks. In contrast to most existing works that assume perfect CSI for BF-PS design, we take into account the cost of channel estimation and utilize Long Short-Term Memory (LSTM) networks to design BF-PS from limited samples of estimated channel CSI. An LSTM channel extrapolator is designed first to generate high resolution estimates of the cascaded BS-RIS-user channel from sampled signals acquired at a slow time scale. Subsequently, the outputs of the channel extrapolator are fed into an LSTM-based two stage neural network for the joint design of BF-PS at a fast time scale of per coherence time. To address the critical issue that training overhead increases linearly with the number of RIS elements, we consider various pilot structures and sampling patterns in time and space to evaluate the efficiency and sum-rate performance of the proposed two time-scale design. Our results show that the proposed two time-scale design can achieve good spectral efficiency when taking into account the pilot overhead required for training. The proposed design also outperforms a direct BF-PS design that does not employ a channel extrapolator. These demonstrate the feasibility of applying RIS in time-varying channels with reasonable pilot overhead.