A methodology to integrate solar thermal energy in district heating networks confronted with a Swedish real case study

Among other solutions, the integration of solar energy in district heating networks can contribute significantly to the increase of the renewable energy fraction in an urban area. This article describes the methodology developed in the framework of IEA Task 52 to help stakeholders during early phases of new or refurbishments projects. This methodology integrates a tool which is capable to assess the main solar indicators of the project with only two inputs: the solar installation area and the building heated area. The tool outputs are compared to the measured values of a Swedish case study

Toward an Integration of Deep Learning and Neuroscience

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.National Institutes of Health (U.S.) (Grant R01MH103910