In this paper, we study the real domestic hot water (DHW) consumptions from single family houses equipped with solar hot water tank. We model it to understand and forecast the daily needs of inhabitants. Thus, the forecasts can be integrated in a control strategy to optimize the energy cost by heating only the necessary DHW volume. At first, we realize a data analysis from real uses of several dwellings to lay assumptions of the statistical model. This study highlights a weekly periodicity, random fluctuations and the different profiles of consumption following the residence, the season, and the day of the week. Otherwise, having no prior information like the location or the number of residents, we propose an adaptive time series model which does not require strong a priori and computational time. Then, we develop an ARMA model to forecast the daily DHW volume and we apply it on each individual installation. This model allows to take into account the periodicity of one week, the consumption of the previous days and random fluctuations. The results obtained on real data show that this approach is very promising.

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This work aims to evaluate the energy savings that can be achieved in domestic hot water (DHW) production using consumption forecasting through statistical modeling. It uses our forecast algorithm and aims at investigating how it can improve energy efficiency depending on the system configuration. Especially, the influence of the DHW production type used is evaluated as well as the water tank insulation. To that end, real consumption measurements are used for model training. Then simulations are run on using TRNSYS software to compute the total energy consumption of DHW production systems over 1 year. Simulations are also based on real consumption measurements for realistic results. To appraise the energy savings, we compared simulations that consider either no forecast (reactive control), perfect forecast (to estimate the control ability to consider forecast), or the forecast provided by our algorithm. The measurements and simulations are run on 26 different but real dwellings to assess results variability. Several system configurations are also compared with varying thermal insulation indices for a complete benchmark of the approach so that an overall performance of the system and the anticipation strategy could be evaluated.

In this paper, we focus on allying fuzzy logic, which is a suitable model for human-like information, and causality, which is a key concept for humans to generate knowledge from observations and to build explanations. If a fuzzy premise causes a fuzzy consequence, then acting on the fuzzy premise will have an impact on the fuzzy consequence. This is not necessarily the case for common fuzzy rules whose induction is based on correlation. Indeed, correlations may be due to some latent common cause of fuzzy premise and consequence. In this case, a change in the value of the fuzzy premise may not affect the fuzzy consequence as it should. We propose an approach to construct a set of causality-based fuzzy rules from crisp observational data. The idea is to identify causal relationships on the set of fuzzified inputs and outputs by well-known constraintsbased causal discovery algorithms such as Peter-Clark and Fast Causal Inference. The causal discovery algorithms are combined with entropy-based conditional independent testing that avoids making hypotheses on the data distribution. Experiments are conducted to evaluate our approach in terms of ability to recover causal relationships between fuzzy sets in the presence of a latent common cause. The results illustrate the interest of our approach compared to a correlation-based approach and state-of-the-art approaches.