It bears noticing that MW (your dependent variable) is truncated at zero. This should be the first thing your model accounts for. Right now, many of the fitted values are much less than zero. This means that the functional form of your regression model is misspecified.
There are a number of ways to deal with this. One consideration is to employ a technology diffusion model which assumes a "zero" origin for a time series followed by a diffusion process based on cumulants of the new MW capacity added each year for each state. Good references to this class of models are available at Rockefeller University's Program for the Human Environment. Search for publications regarding "logistic growth" here:
The PHE is not the only source for information about this class of models as they have been widely deployed in marketing in an effort at understanding new product sales growth. For instance, find references to "Bass new product models" or dig up this review: Peres, Muller, Mahajan, Innovation, diffusion and new product growth models: A critical review and research directions, 2009.
There are limitations to diffusion approaches: they are rooted in the internal, univariate dynamics of the trajectories of the cumulants over time and, as a general rule, do not permit use of additional predictors when estimating. Moreover, these models do not produce HAC residuals. In point of fact, given the short duration of much tech diffusion data, it's typically not even possible to fit "Box-Jenkins"-type approaches -- there just isn't enough information to initialze the "p's and q's" or lags and moving averages. They are univariate models, this means they don't lend themselves to stacking as in a pooled approach. Finally, they are criticized for being "tautological" or deterministic in positing a nonlinear or "S-shape" to their predictions.
To this last point, linear models can be criticized as being deterministic for positing an unrealistic linearity to a time series.
However, diffusion models do answer questions related to growth rates, inflection points and likely asymptotic ceilings or maximal values projected out to some reasonable point in the future, and as attainable based on the current information. Moreover, "horizons" to growth can be built as new information comes in and the model is re-estimated. Then, too, there are workarounds to the need to estimate a unique model for each state by assuming that there is one underlying diffusion process to wind power and the different adoption times by state are irrelevant. If not "irrelevant" then they represent a different behavior from the underlying process and, as such, would require models for limited dependent variables with different information capturing the drivers of adoption, not the diffusion process over time.
With these caveats noted, this approach is highly appropriate for the behaviors you're observing.
Technology diffusion has its roots in economic growth. Some of the best contributions in this field do not rely on logistic growth equations. But, as Paul Romer states in a recent, controversial paper Mathiness in the Theory of Economic Growth, American Economic Review, 2015, "In 1970, there were zero mobile phones. Today, there are more than 6 billion. This is the kind of development that a theory of growth should help us understand." Reference to this stream of work might be considered.