Abstract-Very short-term load forecasting predicts the loads 1 h into the future in 5-min steps in a moving window manner based on real-time data collected. Effective forecasting is important in area generation control and resource dispatch. It is however difficult in view of the noisy data collection process and complicated load features. This paper presents a method of wavelet neural networks with data pre-filtering. The key idea is to use a spike filtering technique to detect spikes in load data and correct them. Wavelet decomposition is then used to decompose the filtered loads into multiple components at different frequencies, separate neural networks are applied to capture the features of individual components, and results of neural networks are then combined to form the final forecasts. To perform moving forecasts, 12 dedicated wavelet neural networks are used based on test results. Numerical testing demonstrates the effects of data pre-filtering and the accuracy of wavelet neural networks based on a data set from ISO New England.Index Terms-Neural networks, pre-filtering, very short-term load forecasting, wavelet and filter bank.

A key ingredient contributing to the success of CompCert, the state-of-the-art verified compiler for C, is its block-based memory model, which is used uniformly for all of its languages and their verified compilation. However, CompCert's memory model lacks an explicit notion of stack. Its target assembly language represents the runtime stack as an unbounded list of memory blocks, making further compilation of CompCert assembly into more realistic machine code difficult since it is not possible to merge these blocks into a finite and continuous stack. Furthermore, various notions of verified compositional compilation rely on some kind of mechanism for protecting private stack data and enabling modification to the public stack-allocated data, which is lacking in the original CompCert. These problems have been investigated but not fully addressed before, in the sense that some advanced optimization passes that significantly change the ways stack blocks are (de-)allocated, such as tailcall recognition and inlining, are often omitted. We propose a lightweight and complete solution to the above problems. It is based on the enrichment of CompCert's memory model with an abstract stack that keeps track of the history of stack frames to bound the stack consumption and that enforces a uniform stack access policy by assigning fine-grained permissions to stack memory. Using this enriched memory model for all the languages of CompCert, we are able to reprove the correctness of the full compilation chain of CompCert, including all the optimization passes. In the end, we get Stack-Aware CompCert, a complete extension of CompCert that enforces the finiteness of the stack and fine-grained stack permissions. Based on Stack-Aware CompCert, we develop CompCertMC, the first extension of CompCert that compiles into a low-level language with flat memory spaces. Based on CompCertMC, we develop Stack-Aware CompCertX, a complete extension of CompCert that supports a notion of compositional compilation that we call contextual compilation by exploiting the uniform stack access policy provided by the abstract stack.

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The logic of hereditary Harrop formulas (HH) has proven useful for specifying a wide range of formal systems that are commonly presented via syntax-directed rules that make use of contexts and side-conditions. The two-level logic approach, as implemented in the Abella theorem prover, embeds the HH specification logic within a rich reasoning logic that supports inductive and co-inductive definitions, an equality predicate, and generic quantification. Properties of the encoded systems can then be proved through the embedding, with special benefit being extracted from the transparent correspondence between HH derivations and those in the encoded formal systems. The versatility of HH relies on the free use of nested implications, leading to dynamically changing assumption sets in derivations. Realizing an induction principle in this situation is nontrivial and the original Abella system uses only a subset of HH for this reason. We develop a method here for supporting inductive reasoning over all of HH. Our approach relies on the ability to characterize dynamically changing contexts through finite inductive definitions, and on a modified encoding of backchaining for HH that allows these finite characterizations to be used in inductive arguments. We demonstrate the effectiveness of our approach through examples of formal reasoning on specifications with nested implications in an extended version of Abella.Keywords formal specifications, meta-theoretic reasoning, higher-order abstract syntax, induction over higher-order specifications.

Abstract. We describe an approach to the verified implementation of transformations on functional programs that exploits the higher-order representation of syntax. In this approach, transformations are specified using the logic of hereditary Harrop formulas. On the one hand, these specifications serve directly as implementations, being programs in the language λProlog. On the other hand, they can be used as input to the Abella system which allows us to prove properties about them and thereby about the implementations. We argue that this approach is especially effective in realizing transformations that analyze binding structure. We do this by describing concise encodings in λProlog for transformations like typed closure conversion and code hoisting that are sensitive to such structure and by showing how to prove their correctness using Abella.